198 Machine Learning jobs in Tirupati

Branch OverviewBranch delivers world-class financial services to the mobile generation. With offices in the United States, Nigeria, Kenya, and India, Branch is a for-profit socially conscious company that uses the power of data science to reduce the cost of delivering financial services in emerging markets. We believe that everyone everywhere deserves fair financial access. The rapid spread of smartphones presents an opportunity for the world’s emerging middle class to access banking options and achieve financial flexibility.Branch’s mission-driven team is led by the founder and former CEO of Kiva.org. The company presents a rich opportunity for our team members to drive meaningful growth in rapidly evolving and changing markets. In 2019, Branch announced our Series C and garnered more than $100 M in funding with investments from leading Silicon Valley firms, including Andreessen Horowitz, Trinity Capital, Foundation Capital, Visa, and the International Finance Corporation (IFC).As a company, we are passionate about our customers, fearless in the face of barriers, and driven by data. As a product-driven org, we value bottom-up innovation and decentralized decision-making. We believe the best ideas can come from anyone in the company, and we create an environment where everyone feels empowered to propose solutions to the challenges we face.We value diversity and are committed to providing an inclusive working environment where human beings of all backgrounds can thrive.Job OverviewBranch launched in India in early 2019 and has seen rapid adoption and growth. We are expanding our product portfolio as well as our user base in all our markets including India. We are looking for talented Machine Learning Engineers to join us and be part of this journey. You will work closely with other Engineers, Product Managers, and underwriters to develop, improve, and deploy machine learning models and to solve other optimization problems. We make extensive use of machine learning in our credit product, where it is used (among other things) for underwriting and loan servicing decisions. We are also actively exploring other applications of Machine Learning in some of our newer products, with the ultimate goal of improving the user experience.Machine Learning sits at the intersection of a number of different disciplines: Computer Science, Statistics, Operations Research, Data Science, and others. At Branch, we fundamentally believe that in order for Machine Learning to be impactful, it needs to be closely embedded into the rest of the product development and software engineering process, which is why we emphasize the importance of software engineering skills and experience for this role.As a company, we are passionate about our customers, fearless in the face of barriers, and driven by data. As an engineering team, we value bottom-up innovation and decentralized decision-making. We believe the best ideas can come from anyone in the company, and we are working hard to create an environment where everyone feels empowered to propose solutions to the challenges we face. We are looking for individuals who thrive in a fast-moving, innovative, and customer-focused setting.ResponsibilitiesCredit Decisions: Core to our business is understanding and building signals from unstructured and structured data to identify good borrowers.Customer Service: Using machine learning and LLM/NLP, automate customer service interactions and provide context to our customer service team.Fraud Prevention: Identify patterns of fraudulent behavior and build models to detect and prevent these behaviors.Team work: Bring your experience to bear on the technical direction and abilities of the team, and work cross-functionally with policy and product teams as we improve processes and break new ground.Qualifications2+ years of hands-on experience building software in a production environment. Startup or early-stage team experience is preferred.Excellent software engineering and programming skills, especially Python and SQL.A diverse range of data skills, including experimentation, statistics, and machine learning, and have used these skills to inform business decisions.A deep understanding of using cloud computing infrastructure and data pipelines in production.Self motivation: You teach yourself new skills. You take the initiative to solve problems before they arise. You roll up your sleeves and get stuff done.Team motivation: You listen to others, speak your mind, and ask the right questions. You are a great collaborator and teacher.The drive to make a positive impact on customers' lives.Benefits of JoiningMission-driven, fast-paced, and entrepreneurial environmentCompetitive salary and equity packageA collaborative and flat company cultureFully-paid Group Medical Insurance and Personal Accidental InsuranceUnlimited paid time off, including personal leave, bereavement leave, and sick leaveFully paid parental leave — 6 months maternity leave and 3 months paternity leaveMonthly WFH stipend alongside a one-time home office set-up budget$500 Annual professional development budgetTeam meals and social events — Virtual and In-personWe’re looking for more than just qualifications -- if you’re unsure that you meet the criteria but identify with our vision of providing equal opportunity to everyone to access financial services, please do not hesitate to apply!Branch International is an Equal Opportunity Employer. The company does not and will not discriminate in employment on any basis prohibited by applicable law.

Position- Machine Learning EngineerExperience - 3 to 5 yearsJob Location - Pune / RemoteImmediate Joiner OnlyRole OverviewThe ML Ops Engineer plays a critical role in the development, automation, and deployment of Machine Learning (ML) and Generative AI (Gen AI) pipelines across AWS cloud environments. This hands-on role emphasizes building reproducible workflows, integrating observability tools, and enabling efficient, scalable model delivery. The position supports AI systems deployed in banking environments, where resilience and reliability are paramount.Key Experience:3-5 years Strong programming skills in Python, with experience in pandas, SQL, and ML frameworks (e.g., scikit-learn).Should have used Python in ML domain to build, train and maintain models.Familiarity with AWS services such as Lambda, Glue, Cloud Watch, and Bedrock.Experience with container workflows (Docker) and model lifecycle management.Foundational knowledge of observability practices and model deployment fundamentals.Interest or experience in supporting AI systems used by developers or analysts.Strong communication and documentation skills with a collaborative team mindset.Ability to assume ownership of assignments and consistently meet deadlines.Responsibilities:Deploy and maintain ML and Gen AI models using AWS services, including Sage Maker, Fargate, and Bedrock.Apply prompt engineering techniques to optimize Gen AI model performance and reliability.Experience with Retrieval-Augmented Generation (RAG) applications is a plus.Assist in building and maintaining internal model-serving platforms to support development teams.Implement containerized services using Docker and deploy them to AWS infrastructure.Write Infrastructure-as-Code (Ia C) using Terraform to automate cloud resource provisioning (nice to have).Participate in unit and end-to-end testing of ML pipelines, services, and monitoring workflows.Support model monitoring and health tracking using AWS Cloud Watch and internal observability tools.Document internal systems and operational processes to ensure maintainability and reproducibility.

Are you a talented Data Scientist (includes AI/ML Researcher, AI/ML Engineer, Data Engineer, ML Ops Engineer, QA Engineer with AI/ML focus, NLP Engineer, LLM Engineer) either,

• Looking for your next big challenge working remotely OR

• Employed , but open to offers from elite US companies to work remotely?

Submit your resume to our GlobalPros.ai’s, an exclusive community of the world’s top pre-vetted developers dedicated to precisely matching you with our US employers.

Globlpros.ai is followed internationally by over 13,000 employers, agencies and the world’s top developers.

We are currently searching for a full-time AI/ML developer (includes AI/ML Researcher, AI/ML Engineer, Data Engineer, Data Scientist, ML Ops Engineer, QA Engineer with AI/ML focus, NLP Engineer) to work remotely for our US employer clients.

What We Offer:

• Competitive Compensation . Compensation is negotiable and commensurate with your experience and expertise.

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• Anonymity . If you’re employed but open to offers, your profile is anonymous and is not available on our website or otherwise online. When matched with our clients, your profile is anonymous until you agree to be interviewed. So there’s no risk in submitting your resume now.

We're Looking For:

• Experience . Must have at least 3 years of experience .

• Role . AI/ML developer (includes AI/ML Researcher, AI/ML Engineer, Data Engineer, Data Scientist, ML Ops Engineer, QA Engineer with AI/ML focus, NLP Engineer)

• Skills . TensorFlow, PyTorch, Scikit-learn Python, Java, C++, R, AWS, Azure, GCP, (SQL, NoSQL, Hadoop, Spark, Docker, Kubernetes, AWS Redshift, Google BigQuery.

• Willing to work full-time . (40 hours per week) .

• Available for an hour of assessment testing . Being deeply-vetted with a data enhanced resume and matched precisely by our machine learning algorithms substantially increases the probability of being hired quickly, at higher compensation levels over unvetted candidates. It's your substantial competitive advantage in a crowded job market.

Job Description:

We’re seeking a hands-on ML Engineer to transform cutting-edge research

into production features. You will own Tiny LLM on-device inference,

predictive analytics, dynamic escalation workflows, gamified modules, and

localisation pipelines—working end-to-end from model training to mobile

deployment.

Required Qualifications:

• Bachelor’s or Master’s in Computer Science, Engineering, or related field

• 2+ years’ hands-on experience with transformer architectures, fine-tuning,

and model inference

• Strong proficiency in Python, including libraries/frameworks such as

PyTorch, TensorFlow, sciki t- learn, and fastAPI

• Proven track record deploying ML models to production (TorchServe, ONNX,

HuggingFace Inference API)

• Solid data engineering skills: data lakes, batch pipelines, structured logging

(e.g., Airflow, Spark)

• Familiarity with edge/embedded ML: quantisation (4–8 bits), memory

footprints (20–50 MB), RAM budgets (100–300 MB)

• Experience configuring API Gateways, server-less functions, and message

queues (Kafka, Celery)

• Deep understanding of data security, privacy regulations (GDPR/HIPAA-

inspired), consent flows, and audit logging

• Expertise in localisation and NLP: neural translation, dialect adaptation,

multi-modal (text & voice) processing

• Comfortable in Agile/CI-CD environments with containerised micro-services

(Docker, Kubernetes)

Key Responsibilities:

• Develop and optimise Tiny LLM inference pipelines.

• Implement dynamic risk-based escalation workflows (sentiment 0.0–1.0;

thresholds 0.3–0.7; horizon 3–14 days)

• Build gamification engines (points: 10–100; streaks: 3–30 days; quest

windows: 1–7 days) to boost retention

• Integrate neural machine translation with regional dialect support (latency:

100–300 ms; BLEU: 30–50) for text and voice interfaces

• Architect offline data synchronisation (intervals: 1–24 hrs; payload: 5–50 kB)

and ensure seamless async sync under <50 kB/s bandwidth

• Deploy models and services using TorchServe, ONNX, or HuggingFace

Inference API, and manage server-less scaling, API Gateway, Kafka/Celery

queues

• Collaborate with backend and mobile teams to meet performance targets (UI

load: 100–300 ms; battery drain: 1–3 %/hr)

• Embed security and compliance: AES-256 & TLS 1.3 encryption, consent

management, legal disclaimers, audit-grade logging

• Maintain high availability (99.9 % SLA), automated retraining cycles (1–4

weeks), and structured logging for analytics

• Write clean, production-grade Python code to support data pipelines, model

training, inference, and integration.

Preferred Skils:

• Prior work in digital health or mental-wellness applications

• Familiarity with mobile frameworks (React Native, Flutter)

• Experience designing or measuring gamification metrics

• Knowledge of federated learning or privacy-preserving ML

What We Offer:

- Competitive contract rate

- Remote work arrangement

- Opportunity to work on exciting projects with a talented team

If you’re passionate about building humane AI that transcends infrastructure

barriers and delivers personalised, proactive mental care, we’d love to hear

from you. Please share your resume and a brief note on a production ML system you’ve delivered end-to-end.

Machine Learning Engineer

About Astreya:

Astreya offers comprehensive IT support and managed services. These services include Data

Center and Network Management, Digital Workplace Services (like Service Desk, Audio Visual, and

IT Asset Management), as well as Next-Gen Digital Engineering services encompassing Software

Engineering, Data Engineering, and cybersecurity solutions. Astreya's expertise lies in creating

seamless interactions between people and technology to help organizations achieve operational

excellence and growth.

Responsibilities:

• Design, develop, and deploy machine learning models and algorithms to solve complex business problems and drive data-driven decision making
• Collaborate with cross-functional teams including data engineers, software engineers, and business stakeholders to understand requirements and translate business objectives into scalable ML solutions
• Build and maintain robust ML pipelines for data preprocessing, feature engineering, model training, validation, and deployment
• Optimize model performance through hyperparameter tuning, feature selection, and algorithmic improvements
• Implement MLOps best practices including model versioning, monitoring, and automated retraining workflows
• Ensure data quality, integrity, and security throughout the ML lifecycle
• Scale ML systems to handle large datasets and high-throughput inference requirements
• Conduct A/B testing and experimentation to validate model performance and business impact
• Monitor deployed models for drift, performance degradation, and bias detection
• Create comprehensive documentation including technical specifications, model cards, and deployment guides for stakeholders

Professional & Technical Skills:

• Must have strong proficiency in Python and experience with core ML libraries such as scikit-learn, XGBoost, LightGBM, and CatBoost
• Must have hands-on experience with deep learning frameworks including TensorFlow, PyTorch
• Must have solid understanding of machine learning algorithms, statistical methods, and model evaluation techniques
• Must have experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies Docker or Kubernetes
• Need to have proficiency in SQL and experience working with both structured and unstructured datasets
• Need to have knowledge of big data technologies such as Spark, Hadoop, or distributed computing frameworks
• Must be familiar with MLOps tools and platforms such as MLflow, Kubeflow, or similar model lifecycle management systems
• Must have experience with version control systems (Git) and CI/CD pipelines for ML workflows
• Need to have knowledge of data visualization tools and libraries such as Matplotlib, Seaborn, Plotly, or Tableau
• Must be familiar with software engineering best practices, including code testing, debugging, and performance optimization
• Need to have understanding of software development methodologies such as Agile or Scrum
• Possess excellent analytical and problem-solving skills with ability to work independently and in team environments

Additional Information:

• Must have Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related quantitative field
• Preferred 3+ years of experience in machine learning engineering or related roles
• Preferred experience with real-time inference systems and model serving architectures

Preferred knowledge of specialized domains such as computer vision, natural language processing, or recommendation systems

Job Title: Machine learning engineer Lead

Location: Hybrid Role / Chennai / Bangalore

Type: Fulltime with our client

Job Required Skills:

· 5+ years experience

· College degree

· Strong proficiency in Python (bonus if also have one of these Java, Scala, etc.).

· Advanced experience in SQL; Familiarity with version control tools (Git etc.).

· Understanding of statistical, machine learning and deep learning algorithms.

Ø Manage processes

Ø Has done computer science work, technology work and analysis work – needs a good blend of this

Ø Someone to actual do the work – make improvements to processes and do the work, not just suggest/talk/plan

· Experience working with big data environments using technologies such as Spark, Flink, NoSQL DB structures.

· Experience in cloud computing technologies based analytic solutions.

• · Experience building and productionizing micro-services and REST APIs.

Position- Machine Learning Engineer

Experience - 3 to 5 years

Job Location - Pune / Remote

Immediate Joiner Only

Role Overview

The ML Ops Engineer plays a critical role in the development, automation, and deployment of Machine Learning (ML) and Generative AI (GenAI) pipelines across AWS cloud environments. This hands-on role emphasizes building reproducible workflows, integrating observability tools, and enabling efficient, scalable model delivery. The position supports AI systems deployed in banking environments, where resilience and reliability are paramount.

Key Experience:

• 3-5 years Strong programming skills in Python, with experience in pandas, SQL, and ML frameworks (e.g., scikit-learn).
• Should have used Python in ML domain to build, train and maintain models.
• Familiarity with AWS services such as Lambda, Glue, CloudWatch, and Bedrock.
• Experience with container workflows (Docker) and model lifecycle management.
• Foundational knowledge of observability practices and model deployment fundamentals.
• Interest or experience in supporting AI systems used by developers or analysts.
• Strong communication and documentation skills with a collaborative team mindset.
• Ability to assume ownership of assignments and consistently meet deadlines.

Responsibilities :

• Deploy and maintain ML and GenAI models using AWS services, including SageMaker, Fargate, and Bedrock.
• Apply prompt engineering techniques to optimize GenAI model performance and reliability.
• Experience with Retrieval-Augmented Generation (RAG) applications is a plus.
• Assist in building and maintaining internal model-serving platforms to support development teams.
• Implement containerized services using Docker and deploy them to AWS infrastructure.
• Write Infrastructure-as-Code (IaC) using Terraform to automate cloud resource provisioning (nice to have ).
• Participate in unit and end-to-end testing of ML pipelines, services, and monitoring workflows.
• Support model monitoring and health tracking using AWS CloudWatch and internal observability tools.
• Document internal systems and operational processes to ensure maintainability and reproducibility.