What AI Operations Business Transformation Manager Jobs are in India?

**Requisition number:**
**Job category:** Technology
Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start **Caring. Connecting. Growing together.**
We are seeking a AI/ML Engineer with solid software engineering fundamentals and growing depth in AI/ML and data platforms. This role emphasizes building production ready, scalable AI services, applying Generative AI techniques, and continuously expanding expertise across machine learning and data engineering domains.
**Primary Responsibilities:**
+ AI/ML Solution Development
+ Design, implement, and optimize AI and machine learning solutions, including statistical models, deep learning, and Generative AI systems
+ Model Training, Evaluation & Optimization
+ Execute proof of concepts, train models at scale, and baseline performance using quantitative evaluation metrics
+ Platform & Infrastructure Engineering
+ Build and operate large scale training and inference pipelines using Databricks, PySpark, and cloud platforms (AWS, Azure, GCP)
+ Generative AI & Advanced Techniques
+ Apply RAG, LangChain, and Vector Databases to develop GenAI solutions
+ Optimize and quantize models to improve performance, scalability, and cost efficiency
+ Software Engineering & APIs
+ Develop REST and FastAPI services, containerize solutions using Docker, and integrate UI tools such as Streamlit or Flask
+ Collaboration & Communication
+ Partner with cross functional teams to translate business needs into clear, scalable AI solutions, and present insights effectively
+ Leadership, Mentorship & Culture
+ Mentor engineers, participate in design and architecture reviews, and uphold standards for quality, safety, and trust
+ Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
**Candidate Profile & Growth Mindset**
+ The most solid alignment is with software engineering fundamentals, including system design, clean and maintainable coding, and building production ready services end to end, from development through deployment and cloud infrastructure
+ Comfortable working across AWS and cloud native architectures, with increasing hands on application of AI concepts, particularly LLMs and RAG based solutions
+ Completed AI Dojo Generative AI training, strengthening applied Generative AI foundations and practical implementation skills
+ Actively developing deeper expertise in core machine learning and data engineering, including:
+ Traditional ML models, algorithms, and evaluation techniques
+ Feature engineering and data pipelines
+ Large scale data processing using Spark and Databricks
+ Highly motivated to close skill gaps through structured training, mentorship, and hands on learning, and values collaboration and knowledge sharing within the team
**Required Qualifications:**
+ Bachelor's or Master's degree in Computer Science, Data Science, or a related field
+ Software & AI/ML Engineering Experience
+ 3+ years of professional software engineering experience, delivering high quality, production grade commercial applications end to end
+ 1+ years of AI/ML engineering experience, including deploying models at scale and contributing to technical leadership across AI initiatives
+ Demonstrated ability to design, build, deploy, and operate production ready services, including CI/CD and cloud infrastructure
+ Programming & Systems Expertise
+ 2+ years of hands on experience with Java, Python, SQL, and scripting
+ Solid foundation in clean, maintainable code, system design, API development, and modern software engineering best practices
+ Cloud & Data Platform Experience
+ 1+ years of experience across AWS, Azure, and GCP, with deeper hands on experience in AWS and cloud native architectures
+ 1+ years of experience with Databricks, MongoDB, PySpark/SparkSQL, and data pipeline implementation
+ Familiarity with Hadoop ecosystems and distributed data processing
+ MLOps, Infrastructure & Governance
+ Experience with data governance concepts, including access control and platform level controls in Databricks (Delta Lake, Unity Catalog)
+ Working knowledge of MLOps practices, including model lifecycle management and operationalization
+ Familiarity with Infrastructure as Code using Terraform and CloudFormation
+ Technical Expertise
+ Solid knowledge of AI/ML frameworks, orchestration tools, and scalable architectures
+ Proficiency with big data technologies, including Spark, Hadoop, and Kafka
+ Solid foundation in data science principles, including statistics, probability theory, optimization, simulation, and data modeling
Leverage enterprise-approved AI tools to enhance productivity and innovation by streamlining workflows and automating repetitive tasks. Evaluate emerging trends to drive continuous improvement and strategic innovation.
_At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission._

Responsibilities:
Engineer will be part of the datastore-migration Factory team that will be responsible to perform for the end-to-end datastore migration from on-prem DataLake to AWS hosted LakeHouse. This is a high visibility and crucial project for Goldman Sachs.
Responsibilities of the Engineer includes:
Pipeline Migration
Logic & Scheduling: Refactoring and migrating extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.
Data Transfer: Executing the physical migration of underlying datasets while ensuring data integrity.
Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "handoff and sign-off" conversations with data owners to ensure migrated assets meet business requirements.
Consumption Pattern Migration
Code Conversion: Translating and optimizing legacy SQL and Spark-based consumption patterns (raw and modeled) for compatibility with Snowflake and Iceberg.
Usage analysis: Understand usage patterns to deliver the required data products.
Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "handoff and sign-off" conversations with data owners to ensure migrated assets meet business requirements.
Data Reconciliation & Quality
A rigorous approach to data validation is required. Candidates must work with reconciliation frameworks to build confidence that migrated data is functionally equivalent to that already used within production flows.
Engineer will also need to work with our other internal data management platform, and must have an aptitude for learning new workflows and language constructs as necessary.
Technical Skills:
Basic Qualifications
Education: Bachelor's or Master's degree in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
Experience: Minimum of 3-5 years of professional "hands-on-keyboard" coding experience in a collaborative, team-based environment. Ability to trouble shoot (SQL) and basic scripting experience.
Languages: Professional proficiency in Python or Java.
Methodology: Deep familiarity with the full Software Development Life Cycle (SDLC) and CI/CD best practices & K8s deployment experience.
Core Data Engineering Competencies: Candidates must demonstrate a sophisticated understanding of the following modeling concepts to ensure data correctness during reconciliation:
Temporal Data Modeling: Managing state changes over time (e.g., SCD Type 2).
Schema Management: Expertise in Schema Evolution (Ref: Iceberg Apache) and enforcement strategies.
Performance Optimization: Advanced knowledge of data partitioning and clustering.
Architectural Theory: Balancing Normalization vs. Denormalization and the strategic use of Natural vs. Surrogate Keys.
Technical Stack Requirements:
While candidates are not expected to be experts in every tool, the collective team must cover the following technologies:
Extraction & Logic
Kafka, ANSI SQL, FTP, Apache Spark
Data Formats
JSON, Avro, Parquet
Platforms
Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ
Core Competencies:
Demonstrates strong integrity and consistently models good conduct and ethical decision-making.
Acts as a trusted team player who collaborates effectively across multiple teams and functions.
Communicates with clarity and confidence - concise written updates, structured verbal briefings, and proactive stakeholder management.
Works effectively with global teams across time zones and cultures; builds alignment and resolves issues constructively.
Delivery-focused with a strong sense of ownership; drives work to closure and meets commitments.
Brings high energy and urgency to achieve targets while maintaining quality and professionalism.
Shows intellectual curiosity; asks thoughtful questions, surfaces risks early, and seeks feedback to continuously improve.

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a AI Security Architect to join our team in Bangalore or Remote, Karnātaka (IN-KA), India (IN).
**Role: AI Security Architect**
**PAN India (Bangalore, Hyderabad, Chennai, Noida, Gurgaon and Pune)**
**Notice Period: 30 Days**
**Responsibilities:**
We are seeking an experienced and highly skilled AI Security hands-on, highly technical architect responsible for defining security architecture and implementing robust security controls for our AI/ML systems and their underlying platforms and will serve as the team's technical mentor and architecture authority, driving secure-by-design patterns across the AI/ML lifecycle (data, training, evaluation, deployment, and production monitoring) and proactively mitigating AI-specific threats such as model integrity risks, data poisoning, adversarial attacks, prompt injection, model extraction, and inference-time abuse. Lead technically, set standards, and guide engineers day-to-day through architecture, reviews, and delivery.
Ensures AI systems are secure, compliant, and resilient by implementing data protection, threat detection, guardrails, and ongoing risk monitoring across the AI lifecycle.
Platform & Enablement Roles
· AI Platform Admin (M365, copilot Studio) Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
· AI Reusable Utility Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
· AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.
Core Responsibilities
1. Agent Security
- Non-Human Identity & Access: Define strict Role-Based Access Control (RBAC) and least-privilege models for AI agents using identity systems (e.g., Entra Agent ID).
- Guardrails & Sandboxing: Design runtime environments with restricted permissions to prevent manipulated agents from accessing unauthorized APIs, data sources, or executing malicious toolchains.
- Input/Output Protection: Implement defenses against adversarial attacks, prompt injections, jailbreaking, and sensitive data leakage (DLP) across agent workflows.
2. Observability & Monitoring
- Decision Traceability: Architect logging and monitoring standards to map how reasoning agents use data and call APIs, eliminating "black box" decisions.
- Model Drift & Integrity: Monitor models and prompt templates in production to detect behavioral drift, anomalies, and poisoning or evasion attacks.
3. SOC Monitoring & Automation
- Autonomous Security (AI SOC): Design LLM-driven and agentic workflows to improve alert triage, contextual correlation, false-positive filtering, and playbook automation.
- Incident Response Playbooks: Establish remediation strategies and threat-hunting procedures for AI-specific events (e.g., compromised model artifacts, hallucination-driven exploits).
4. Compliance Enablement & Governance
- Regulatory Alignment: Map AI-specific controls to established standards like the NIST AI RMF, OWASP Top 10 for LLMs, and GDPR.
- Audit Readiness: Build audit pipelines that track and explain everything an agent does to satisfy ongoing AI regulatory compliance and governance requirements.
Architecture & Secure-by-Design Leadership
- Define and maintain AI security reference architectures for multiple AI deployment patterns, including MCP / Agentic AI and LLM application stacks (RAG, tools/plugins, agents, orchestration).
- Establish and evolve security requirements, patterns, and guardrails across the AI/ML SDLC (design → build → run), including secure pipelines and platform controls.
- Own AI security architecture decisions across critical domains: identity, secrets, data protection, network controls, tenancy boundaries, logging/telemetry, and isolation for training/inference.
Control Design & Implementation (Hands-on)
- Design and deploy controls to ensure model integrity and governance, including RBAC/ABAC for models, feature stores, data sets, registries, and evaluation artifacts.
- Build/enable technical mechanisms for provenance, attestation, signing, and approval workflows (where applicable) across datasets, models, prompts, and deployments.
- Drive implementation of runtime protections for AI services (abuse prevention, rate limiting, input/output validation, prompt-injection mitigations, model endpoint hardening, and monitoring).
Threat Modeling, Assurance, and Risk Reduction
- Conduct and lead AI/ML-specific threat modeling (data poisoning, model evasion, extraction, inversion, supply-chain, prompt attacks), translate findings into actionable backlogs, and drive remediation.
- Define and run security design reviews for AI initiatives; provide clear, pragmatic architecture guidance and document exceptions with risk acceptance paths.
- Establish AI security testing approaches (adversarial testing, red-teaming enablement, evaluation security, misuse/abuse cases) and integrate into delivery pipelines.
Tooling, Automation, and Operational Enablement
- Design and deliver AI security tooling to improve and automate cybersecurity posture (e.g., controls coverage, policy-as-code, detection engineering, vulnerability management integration, incident response playbooks for AI-specific events).
- Define logging/monitoring standards and detection use-cases for AI platforms and LLM apps (drift signals, anomalous access, suspicious prompt patterns, exfiltration indicators, policy violations).
Technical Mentorship & Influence (No Line Management)
- Act as the team's technical mentor: coach engineers through designs, implementations, and trade-offs; raise engineering quality via reviews, pairing, and knowledge sharing.
- Lead by influence across Data Science, Engineering, Product, Platform, and Cybersecurity-driving alignment without formal authority.
- Create internal enablement materials: runbooks, architecture standards, reusable patterns, and reference implementations.
Ideal Qualifications
- Experience: 5+ years in cybersecurity architecture with proven experience securing large-scale LLM deployments and multi-agent workflows.
- Technical Proficiency: Hands-on capability with agent frameworks (e.g., LangChain, LangGraph, AutoGen) and MLOps platforms.
- Framework Knowledge: Deep familiarity with model risk management principles and AI security standards
**About NTT DATA**
NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client's needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, .
**_NTT DATA endeavors to make_** **_ **_accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at_** **_ **_._** **_This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here ( . If you'd like more information on your EEO rights under the law, please click here ( . For Pay Transparency information, please click here ( ._**

**Requisition number:**
**Job category:** Business & Data Analytics
Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start **Caring. Connecting. Growing together.**
**Primary Responsibilities:**
+ Translate business problems into AI/ML, Generative AI, and Agentic AI solution approaches
+ Conduct hands-on experimentation using machine learning, Generative AI, Agentic AI, and emerging AI technologies
+ Design, build, and validate proof-of-concepts (POCs) and prototypes to assess technical feasibility, business value, scalability, and operational readiness
+ Develop production-oriented POCs that establish implementation patterns, reusable assets, architecture guidance, deployment approaches, and operational considerations required for enterprise adoption
+ Create reusable prompts, workflows, evaluation frameworks, reference architectures, solution accelerators, and implementation assets for broader organizational adoption
+ Drive successful transition of validated POCs into production by partnering closely with engineering teams to ensure solutions are scalable, maintainable, secure, and aligned with enterprise architecture standards
+ Develop implementation-ready artifacts including reusable code components, prompt libraries, workflow templates, deployment recommendations, evaluation methodologies, and technical documentation to accelerate engineering adoption
+ Own the technical readiness of AI solutions by proactively identifying scalability constraints, operational dependencies, implementation risks, and mitigation strategies during experimentation
+ Apply AI Development Lifecycle (AIDLC) practices during experimentation phases, including:
+ Structured evaluation and benchmarking
+ Iterative model refinement
+ Experiment tracking and documentation
+ Performance and cost optimization
+ Document learnings, experimentation results, architectural recommendations, and reusable solution assets
+ Develop Generative AI solutions leveraging:
+ Retrieval-Augmented Generation (RAG) architectures
+ Prompt engineering and optimization techniques
+ Vector databases and semantic retrieval frameworks
+ AI evaluation and guardrails
+ Build and evaluate Agentic AI workflows, including:
+ Tool integration and orchestration
+ Multi-step reasoning and planning
+ Multi-agent collaboration patterns
+ Autonomous and semi-autonomous workflows
+ Evaluate emerging AI frameworks, platforms, and technology stacks to identify opportunities for innovation, standardization, and enterprise adoption
+ Support development and adoption of AI accelerators, reusable frameworks, and best practices across teams
+ Optimize early-stage solution cost efficiency through:
+ Token usage awareness and optimization
+ Prompt tuning and response management
+ Model selection based on use-case requirements and cost-performance targets
+ Cost-performance tradeoff analysis
+ Collaborate with business, product, architecture, and engineering teams to clarify requirements and align solutions with measurable business outcomes
+ Communicate experimentation results, trade-offs, recommendations, implementation considerations, and business impact to technical and non-technical stakeholders
+ Accelerate organizational AI adoption by reducing the cycle time from experimentation to production deployment through repeatable patterns and reusable assets
+ Measure success through:
+ Quality and business impact of AI/ML, GenAI, and Agentic AI POCs
+ Production readiness of delivered solutions
+ Percentage of POCs successfully adopted and deployed into production
+ Adoption of reusable accelerators, prompts, workflows, and reference architectures
+ Reduction in experimentation-to-production cycle time
+ Delivery of measurable business outcomes enabled through productionized AI solutions
+ Scientist Responsibilities:
+ Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities
+ Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle
+ Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
**Required Qualifications:**
+ Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Artificial Intelligence, or related field (Master's degree preferred)
+ 12+ years of experience delivering AI/ML solutions with solid ownership of enterprise-scale AI initiatives
+ Hands-on experience with Generative AI technologies, including:
+ Large Language Models (LLMs)
+ Retrieval-Augmented Generation (RAG)
+ Prompt engineering and evaluation
+ Embeddings and vector database technologies
+ Experience with Agentic AI frameworks, orchestration platforms, and tool integration patterns
+ Experience with data pipelines, feature engineering, experimentation frameworks, and model evaluation methodologies
+ Experience optimizing AI systems through model selection, token utilization, and cost-performance tuning
+ Solid experience applying AI Development Lifecycle (AIDLC) principles, experimentation methodologies, and benchmarking frameworks
+ Solid programming experience in Python and SQL
+ Cloud platform experience across Azure, AWS, and/or Google Cloud Platform
+ Proven experience translating business challenges into effective AI/ML solution strategies
+ Demonstrated experience designing, developing, and delivering successful AI proof-of-concepts that progressed into production environments
+ Solid expertise in machine learning, deep learning, experimentation, and model development
+ Deep learning expertise using PyTorch and/or TensorFlow
+ Proven ability to collaborate effectively with engineering organizations to enable successful production adoption of AI solutions
+ Proven solid analytical, problem-solving, communication, and stakeholder management skills
+ Proven ability to collaborate effectively across business, product, engineering, and leadership teams
**Preferred Qualifications:**
+ Experience building enterprise-scale Generative AI and Agentic AI solutions
+ Experience with vector databases such as Pinecone, FAISS, Weaviate, Chroma, pgvector, or Azure AI Search
+ Experience establishing AI experimentation frameworks, evaluation methodologies, governance models, and production-readiness standards
+ Experience developing reusable accelerators, AI platforms, innovation frameworks, or reference architectures
+ Experience mentoring teams and driving AI capability development across organizations
+ Healthcare domain experience including claims, clinical data, EHR/FHIR, healthcare analytics, care management, or operational workflows
+ Healthcare domain experience: claims, EHR/HL7/FHIR, coding (ICD/CPT), risk adjustment, quality measures, de-identification
+ Knowledge of Responsible AI, model governance, AI risk management, and enterprise AI controls
+ Knowledge of Big data platforms (Databricks, Snowflake, BigQuery) and streaming (Kafka); lakehouse patterns
+ Knowledge of MLOps stack: MLflow/SageMaker/Azure ML/Vertex; model monitoring/observability
+ Knowledge of Vector databases (FAISS, Pinecone, pgvector), knowledge graphs (Neo4j), and ontologies (UMLS/SNOMED)
+ Knowledge of Security/compliance frameworks (SOC 2, HITRUST)
+ Knowledge of Additional languages for performance or integration (Scala/Java/Go)
+ Familiarity with frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, LangGraph, or similar platforms
+ Contributions to patents, technical publications, internal frameworks, accelerators, or enterprise AI innovation initiatives
_At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission._
\#NIC #NJP

**Company Description**
Sandisk understands how people and businesses consume data and we relentlessly innovate to deliver solutions that enable today's needs and tomorrow's next big ideas. With a rich history of groundbreaking innovations in Flash and advanced memory technologies, our solutions have become the beating heart of the digital world we're living in and that we have the power to shape.
Sandisk meets people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward. We do this through the balance of our powerhouse manufacturing capabilities and our industry-leading portfolio of products that are recognized globally for innovation, performance and quality.
Sandisk has two facilities recognized by the World Economic Forum as part of the Global Lighthouse Network for advanced 4IR innovations. These facilities were also recognized as Sustainability Lighthouses for breakthroughs in efficient operations. With our global reach, we ensure the global supply chain has access to the Flash memory it needs to keep our world moving forward.
**Job Description**
1. **System Design and Deployment:**
2. Designing and deploying high-performance computing clusters and systems based on organizational requirements and industry best practices.
3. Configuring hardware components, network and storage systems to optimize performance and reliability.
4. **System Maintenance and Monitoring:**
5. Performing routine maintenance tasks such as software updates, patches, and system upgrades to ensure optimal performance and security.
6. Monitoring system performance, resource utilization, and capacity planning to proactively address potential issues and bottlenecks.
7. **User Support and Training:**
8. Providing technical support and troubleshooting assistance to users of the HPC systems.
9. Developing and delivering training sessions to educate users on best practices, usage guidelines, and efficient utilization of HPC resources.
10. **Security and Compliance:**
11. Implementing and maintaining security protocols, access controls, and data protection measures to safeguard HPC infrastructure and sensitive data.
12. Ensuring compliance with relevant regulatory requirements and organizational policies related to HPC operations.
13. **Documentation and Reporting:**
14. Creating and maintaining comprehensive documentation including system configurations, operational procedures, and troubleshooting guides.
15. Generating regular reports on system performance, usage statistics, and operational metrics for management and stakeholders.
**Qualifications**
+ Bachelor's degree in computer science, Information Technology, or a related field (or equivalent work experience).
+ Proven experience (8+ years) as an HPC Administrator or in a similar role managing HPC systems in a production environment.
+ Proficiency in configuring and managing HPC cluster software such as Slurm, NC, LSF or Grid Engine.
+ Strong knowledge of Linux/Unix system administration and shell scripting.
+ Experience with NFS and storage (NetApp/ISILON) and backup management in HPC environments.
+ Familiarity with networking principles, including TCP/IP, VLANs, and InfiniBand.
+ Excellent analytical and problem-solving skills with the ability to troubleshoot complex issues independently.
+ Strong communication skills and the ability to collaborate effectively with cross-functional and cross geography teams and end-users.
**Preferred Skills:**
+ Bachelor's degree in computer science, Engineering, or a related discipline.
+ Experience in HPC technologies (e.g., HPC Systems Professional, Cray Certified System Administrator).
+ Knowledge with containerization technologies (e.g., Docker, Singularity) and workload orchestration frameworks (e.g., Kubernetes) is a plus.
+ Knowledge of scripting languages like shell/Ansible commonly used in unix admin will be a plus.
+ Knowledge of Dell/CISCO UCS servers in HPC environments.
+ Semiconductor domain experience is a must.
**Additional Information**
Sandisk thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.
Sandisk is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

**Requisition number:**
**Job category:** Technology
Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start **Caring. Connecting. Growing together.**
We're looking for a hands-on technical leader to design, build, and productionize AI/ML and GenAI solutions that improve healthcare operations and patient outcomes. You will own end-to-end delivery-from problem framing and data pipelines to models, MLOps/LLMOps, and ongoing monitoring-while mentoring a small team and partnering with product, data engineering, and clinical/operations stakeholders.
**Primary Responsibilities:**
+ Lead architecture and delivery of ML/GenAI solutions (classification, forecasting, NLP, deep learning, LLM apps) at production scale
+ Build robust data/feature pipelines over large clinical/claims datasets; write efficient, well-tested Python (pandas/NumPy) and SQL
+ Develop and deploy LLM capabilities: prompt design, fine-tuning, RAG pipelines, vector indexing, and evaluation with guardrails
+ Establish MLOps/LLMOps best practices: CI/CD, model registry, experiment tracking, monitoring, drift detection, A/B testing, cost/perf optimization
+ Ensure data privacy and compliance (HIPAA/PHI handling, access controls, auditability) and champion model governance and Responsible AI
+ Translate business problems into technical roadmaps; communicate trade-offs and results to executives and non-technical partners
+ Mentor engineers and set engineering standards (code reviews, documentation, reliability, observability)
+ Lead architecture and delivery of ML/GenAI solutions (classification, forecasting, NLP, deep learning, LLM apps) at production scale
+ Build robust data/feature pipelines over large clinical/claims datasets; write efficient, well-tested Python (pandas/NumPy) and SQL
+ Develop and deploy LLM capabilities: prompt design, fine-tuning, RAG pipelines, vector indexing, and evaluation with guardrails
+ Establish MLOps/LLMOps best practices: CI/CD, model registry, experiment tracking, monitoring, drift detection, A/B testing, cost/perf optimization
+ Ensure data privacy and compliance (HIPAA/PHI handling, access controls, auditability) and champion model governance and Responsible AI
+ Translate business problems into technical roadmaps; communicate trade-offs and results to executives and non-technical partners
+ Mentor engineers and set engineering standards (code reviews, documentation, reliability, observability)
+ Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so.
**Scientist** **Responsibilities** _:_
Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities. Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle.
**Required Qualifications:**
+ Bachelors in Computer Science, Engineering, Math, or related field required; MS/PhD preferred or equivalent experience
+ 10+ years of professional experience in software/ML engineering, including 3+ years leading projects or teams
+ Hands-on GenAI experience: LLMs, embeddings, RAG, fine-tuning, evaluation; familiarity with Hugging Face and LangChain/LlamaIndex
+ Solid foundation in statistics and ML (hypothesis testing, experimental design, feature engineering, supervised/unsupervised methods)
+ Deep learning expertise (PyTorch or TensorFlow) and modern NLP (transformers)
+ Expert Python and pandas stack; ability to write vectorized, high-performance code; solid testing practices (pytest) and Git
+ Solid data engineering skills: SQL; experience with Spark/Dask and workflow orchestration (Airflow/Prefect)
+ Cloud proficiency (AWS/Azure/GCP) and containerization/orchestration (Docker/Kubernetes); CI/CD and IaC (Terraform) exposure
+ Proven excellent communication and stakeholder management skills
**Preferred Qualifications:**
+ Healthcare domain experience: claims, EHR/HL7/FHIR, coding (ICD/CPT), risk adjustment, quality measures, de-identification
+ Big data platforms (Databricks, Snowflake, BigQuery) and streaming (Kafka); lakehouse patterns
+ MLOps stack: MLflow/SageMaker/Azure ML/Vertex; model monitoring/observability
+ Vector databases (FAISS, Pinecone, pgvector), knowledge graphs (Neo4j), and ontologies (UMLS/SNOMED)
+ Security/compliance frameworks (SOC 2, HITRUST)
+ Additional languages for performance or integration (Scala/Java/Go)
_At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission._
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Company Description
Working at Allegis Global Solutions (AGS) is more than just a job. It's a career. It's a community of people who invest in your development and empower you to blaze your own trail. Each of us is here to create real, measurable impact that moves needles. We operate beyond "roles" or "jobs" to realize the opportunity to make meaningful contributions to a bigger idea. Because we believe that when you build a workforce that's designed to harness human enterprise, you design a workforce that's built for impact.
At AGS, we help companies all over the world transform their people into a competitive advantage. It's not about filling seats. It's about designing workforces to meet missions and unleash the most transformative power in business today: The power of human enterprise.
With services around the globe, we have a point of view on the future of work that enables us to be a transformative partner in the way work gets done for our clients' organizations. Meeting clients where they are, we design a plan and guide them along a transformational journey, applying bold actions and diverse minds to solve the most complex challenges - from permanent and extended workforce management to services procurement, consulting, direct sourcing and our Universal Workforce Model.
We also represent over 100 countries and speak dozens of languages. So as you're building relationships and doing your job, you'll be exposed to other cultures and advancement opportunities while expanding your knowledge of global markets and strategies.
As a workplace, we focus on relationships - with each other, our clients and our candidates - in fact serving others is one of our core values. We support open communication and recognize that giving constructive criticism can be even harder than receiving it. We appreciate the fearless and the passionate, who force us to be better. Everything we do sits on a pillar of diversity - diverse perspectives, backgrounds and ideas drive innovation and make us successful.
See what it's like to work at AGS by searching #LifeAtAGS on any social network.
Job Description
About the Role
Testing AI systems is a fundamentally different problem than testing traditional software. Outputs are non-deterministic. "Correct" is often a spectrum. And the failure modes-hallucinations, drift, prompt injection-don't show up in unit tests. We need an engineer who understands this and can build the testing strategies, evaluation frameworks, and quality infrastructure to keep our agents reliable in production.
As an AI Quality Engineer, you'll design how we test intelligent agents, agentic workflows, and Foundation Layer capabilities. This is not a manual QA role-you'll write code, build evaluation pipelines, and create automated testing frameworks that run in CI/CD. You'll define what "quality" means for AI systems at AGS and build the systems to measure it.
You'll work across every solution the team builds, which means you'll have broad visibility into the architecture and deep understanding of how our agents behave in the real world. If you're an engineer who cares about quality and wants to solve testing problems that most teams haven't figured out yet, this is the role.
Responsibilities
Testing Strategy & Design
+ Define testing strategies for AI agents, conversational interfaces, and agentic workflows
+ Design behavioral test suites for non-deterministic outputs-where "correct" isn't binary
+ Build evaluation frameworks that measure groundedness, factuality, relevance, and task completion
+ Identify failure modes specific to AI systems: hallucinations, prompt injection, context window limitations, drift
+ Develop testing approaches for each architecture pattern: RAG, function calling, human-in-the-loop, autonomous workflows
Test Automation & Infrastructure
+ Build automated evaluation pipelines that run as part of CI/CD
+ Create test harnesses for LLM-based systems-mocking, fixtures, and reproducible test scenarios
+ Develop regression suites that detect quality degradation when prompts, models, or data change
+ Build monitoring and alerting for production agent quality (accuracy, latency, error rates)
+ Maintain test infrastructure: test data management, environment setup, reporting dashboards
Evaluation & Metrics
+ Define quality metrics for each solution-what to measure and what thresholds matter
+ Build and maintain evaluation datasets (ground truth, reference outputs, edge case collections)
+ Conduct systematic prompt evaluation when prompts or models change
+ Track quality trends over time and identify when re-evaluation is needed
+ Report quality metrics to the team and stakeholders in clear, actionable terms
Collaboration & Quality Culture
+ Partner with AI Solutions Engineers to define testability requirements during design
+ Work with AI Solutions Analysts to translate acceptance criteria into test scenarios
+ Review solution designs from a quality and testability perspective
+ Advocate for quality practices across the team-testing isn't an afterthought, it's part of delivery
+ Contribute to incident response by diagnosing quality failures and building regression tests
Qualifications
Qualifications
Required
+ 3-7 years of software engineering or quality engineering experience
+ Strong programming skills in Python and/or TypeScript-you write test code, not just test cases
+ Experience designing and building automated test frameworks
+ Understanding of AI/ML systems-you know why testing LLM outputs is different from testing deterministic code
+ Experience with CI/CD pipelines and integrating automated tests into build processes
+ Ability to reason about non-deterministic systems and design meaningful quality metrics
+ Strong analytical skills-you can look at agent outputs and determine whether they're good enough
Preferred
+ Experience testing AI/ML applications, conversational interfaces, or chatbots
+ Background in LLM evaluation: prompt testing, groundedness scoring, factuality checking
+ Familiarity with evaluation frameworks (DeepEval, Ragas, custom evaluation pipelines)
+ Experience with Microsoft Power Platform (Power Automate, Copilot Studio) testing
+ Background in Azure services and cloud-based test infrastructure
+ Experience with load testing and performance testing for API-based systems
+ Familiarity with staffing, HR tech, or workforce management domains
Technology Stack
+ Languages: Python, TypeScript
+ Platforms: Azure (Container Apps, Functions, AI Services), Microsoft 365
+ Testing: pytest, evaluation frameworks (DeepEval, Ragas, custom), load testing tools
+ AI/ML: LLM evaluation, prompt testing, RAG evaluation, behavioral testing
+ Data: REST APIs, Dataverse, SQL
+ Tools: Git, GitHub, CI/CD pipelines, Docker, monitoring/alerting (Application Insights)
We don't expect expertise in everything. AI quality engineering is a new discipline-we expect strong engineering fundamentals and the ability to figure out new problems.
What We're NOT Looking For
+ Manual testers who write test cases in spreadsheets
+ QA professionals who treat testing as a gate at the end of development rather than a practice woven into it
+ People who expect deterministic pass/fail for every test-AI quality requires nuance
+ Engineers who test to the spec but don't think about how real users will break things
What Makes You Stand Out
+ You've tested a system where "correct" was hard to define-and found a way to measure it anyway
+ You write test code that's as clean and maintainable as production code
+ You think about edge cases that nobody else considers
+ You can explain why a particular quality metric matters and what threshold makes sense
+ You've built test automation that actually caught regressions before they hit production
+ You're comfortable saying "this isn't good enough" and backing it up with data
What We're Building
The AI Engineering team delivers intelligent solutions for AGS's global clients:
+ Intelligent Agents - Conversational AI that helps hiring managers, recruiters, and internal teams get work done faster
+ Agentic Workflows - Automated processes where AI executes tasks with human oversight
+ Foundation Capabilities - Reusable AI services that power multiple solutions
You'll make sure these systems work reliably-not just at launch, but as models change, data evolves, and usage scales.
Career Growth
AI quality engineering is an emerging discipline with no ceiling. Growth paths include:
+ Depth - Become the team's authority on AI evaluation and testing methodology, influencing quality standards across the organization
+ Breadth - Move into a Senior or Lead AI Solutions Engineer role, bringing your quality mindset to architecture and delivery
+ Specialization - Build expertise in areas like LLM security testing, AI safety, or evaluation research
Additional Information
As a workplace, we focus on relationships - with each other, our clients and our candidates - in fact serving others is one of our core values. We support open communication and recognize that giving constructive criticism can be even harder than receiving it. We appreciate the fearless and the passionate, who force us to be better. Everything we do sits on a pillar of diversity - diverse perspectives, backgrounds and ideas drive innovation and make us successful.
See what it's like to work at AGS by searching #LifeAtAGS on any social network.