1458 Machine Learning Specialist jobs in Bengaluru

Job Title: Data Scientist

Minimum 5 to 6 years of experience in Deep Learning and Machine Learning

Location: Bangalore (Hybrid)

Why should you choose us?

Rakuten Symphony is a Rakuten Group company, that provides global B2B services for the mobile telco industry and enables next-generation, cloud-based, international mobile services. Building on the technology Rakuten used to launch Japan’s newest mobile network, we are taking our mobile offering global. To support our ambitions to provide an innovative cloud-native telco platform for our customers, Rakuten Symphony is looking to recruit and develop top talent from around the globe. We are looking for individuals to join our team across all functional areas of our business – from sales to engineering, support functions to product development. Let’s build the future of mobile telecommunications together!

What do we expect from you

Rakuten is seeking a dynamic and experienced Generative AI with specialized expertise in training Large Language Models (LLMs) and implementing workflows based on Retrieval-Augmented Generation (RAG) . As a key member of our AI Solutions team, you will play a pivotal role in architecting and delivering cutting-edge solutions that leverage the power of Rakuten's generative AI technologies. This position requires a deep understanding of language models, particularly LLMs, and a strong proficiency in designing and implementing RAG-based workflows.

You must have deep technical experience working with technologies related to multimodal, Anomaly Detection and Forecasting, image generation, from model fine-tune to prompt engineering. A strong developing machine learning background is preferred, in addition to experience building application and architecture design. You will be familiar with the ecosystem of software vendors in the AI/ML space, and will leverage this knowledge to help build AI powered applications for the user.

Required Skills and Expertise:

• Solve customer problems by creating solutions using our innovative technology for Machine Learning and Deep Learning including Large Language Models, Computer Vision systems, Recommender systems, and Advanced Generative AI systems.
• Strong experience and expertise in Machine Learning for Time Series, Deep Learning, Classical machine learning, clustering, dimensionality reduction and Reinforcement learning. Ability to quickly learn and implement state-of-the-art, and the thought process of generating novel ideas.
• Model Development and Deployment : Experience in Designing, developing, and optimizing generative models to generate realistic and diverse outputs. Implementing and fine-tune state-of-the-art generative AI architectures to achieve desired performance metrics. Experience in deploying language models in production environments and integrating them into applications, platforms, or services.
• Architect end-to-end generative AI solutions with a focus on LLMs and RAG workflows and refine foundation model infrastructure to support the deployment of optimized AI models. Implement state-of-the-art optimization techniques, including quantization, distillation, sparsity, streaming, and caching, for model performance enhancements.
• Implement strategies for efficient and effective training of LLMs to achieve optimal performance and design and implement RAG-based workflows to enhance content generation and information retrieval.
• Research and Development: Stay up-to-date with the latest advancements in generative AI, including LLMs, GPTs, GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and other related techniques. Conduct research to identify and develop novel generative models and algorithms.

Qualifications and Skills

• Strong foundational expertise, from a BS, MS, or Ph.D. degree in Engineering, Mathematics, Physics, Computer Science, Data Science, or similar (or equivalent experience).
• 4-6 years ’ experience demonstrating an established track record in Deep Learning and Machine Learning;
  experience with GPUs aswell as expertise in using deep learning frameworks such as TensorFlow or PyTorch .
• Experience working and building ML Solutions in Telecommunication Domain.
• Strong data analytical and problem-solving skills, preferably in the telecom domain.
• Strong coding development and debugging skills. Including experience with Python, C/C++, Bash, as well as Cloud services, Spark and Linux.
• Experience working with DevOps and MLOps including but not limited to Docker/Containers, Kuberzetes, and Data Center or Cloud AI deployments.
• Ability to multitask effectively in a dynamic environment.
• Clear written and oral communications skills with the ability to effectively collaborate with executives and engineering teams.
• Successful candidates will be able to demonstrate a strong desire to share knowledge with clients, partners, and co-workers.

What we look for?

• Must have sense of ownership for algorithm/product.
• Must be comfortable working with senior and junior level colleagues in various cultures.
• Must be highly collaborative.
• Must be comfortable working with people in different geographies.
• Must be comfortable working in diverse/multi-cultural environment

RAKUTEN SHUGI PRINCIPLES :

Our worldwide practices describe specific behaviours that make Rakuten unique and united across the world. We expect Rakuten employees to model these 5 Shugi Principles of Success.

Always improve, always advance . Only be satisfied with complete success - Kaizen.

Be passionately professional . Take an uncompromising approach to your work and be determined to be the best.

Hypothesize - Practice - Validate - Shikumika . Use the Rakuten Cycle to success in unknown territory.

Maximize Customer Satisfaction . The greatest satisfaction for workers in a service industry is to see their customers smile.

Speed! Speed! Speed! Always be conscious of time. Take charge, set clear goals, and engage your team.

Company Description

Ai Health Highway is a global team of medical professionals, signal processing engineers, data scientists, and clinicians. The company is AI-first and focused on making screening of chronic diseases cost-effective at the primary care clinic. This approach aims to improve patient outcomes and accessibility to healthcare services.

We are backed by Turbostart, Rainmatter by Zerodha, The Chennai Angels, Social Alpha, Foundation for Science, Innovation & Development (FSID) IISC and many other prominent angel investors. AiSteth is one of the 27 most promising ML startups selected for AWS ML Elevate 2022 program by Amazon, Intel, Accel & YourStory. We are also the winners of the PHC Tech Challenge 2021 award organized by PATH.

Goal - Our Goal is to reduce 30% premature deaths due to #NCDs by 2030.

Role Description - ML Engineer (Bangalore / On-Site)

• Work closely with the Product leader to define product/platform vision
• Interface with Client ML and Business teams to demonstrate thought leadership, and drive delivery of products and solutions
• Conduct original research on large proprietary and open source data sets
• Identify, research, prototype and build predictive models
• Create framework for AI/ML code deployment to ensure robustness and reliability of production ready models
• Responsible for understanding industry processes and incorporating them in the solution
• Be responsible for measuring and optimizing the quality of your algorithms
• Collaborate with engineering teams, platform teams to drive vision for AI/ML platform

Qualifications

• Minimum 2-3 years of experience working in similar field/ domain.
• Advanced degree in Computer Science, Signal Processing, Data science, Biomedical Engineering, AI/ML
• At least one core programming expertise, such as python (Tensorflow, Keras, Pytorch, Signal Processing packages etc.), R, Scala
• Strong statistical knowledge, analytical and problem-solving skills, intuition and experience applying machine learning models to real world data
• Strong project management skills to execute multiple projects in parallel
• Experience in deploying production ready ML code.
• Experience with knowledge graphs, optimization, decision theory or signal processing
• Interpersonal skills: good verbal and written communication skills, cross-group and cross-culture collaboration.
• Understanding of Algorithms and Machine Learning principles
• Experience with healthcare data analysis is a plus

TE-4 Years and above

Location- Bangalore/Chennai/Hyderabad

NP- 15-30 Days max

JOB DESCRIPTION

Join our fast‑growing team to build a unified platform for data analytics, machine learning, and generative AI. You’ll integrate the AI/ML toolkit, real‑time streaming into a backed feature store, and dashboards—turning raw events into reliable features, insights, and user‑facing analytics at scale.

What you’ll do

• Design and build streaming data pipelines (exactly‑once or effectively‑once) from event sources into low‑latency feature serving and NRT and OLAP queries.
• Develop an AI/ML toolkit: reusable libraries, SDKs, and CLIs for data ingestion, feature engineering, model training, evaluation, and deployment.
• Stand up and optimize a production feature store (schemas, SCD handling, point‑in‑time correctness, TTL/compaction, backfills).
• Expose features and analytics via well‑designed APIs/Services;
  integrate with model serving and retrieval for ML/GenAI use cases.
• Build and operationalize Superset dashboards for monitoring data quality, pipeline health, feature drift, model performance, and business KPIs.
• Implement governance and reliability: data contracts, schema evolution, lineage, observability, alerting, and cost controls.
• Partner with UI/UX, data science, and backend teams to ship end‑to‑end workflows from data capture to real‑time inference and decisioning.
• Drive performance: benchmark and tune distributed DB (partitions, indexes, compression, merge settings), streaming frameworks, and query patterns.
• Automate with CI/CD, infrastructure‑as‑code, and reproducible environments for quick, safe releases.

Tech you may use

Languages: Python, Java/Scala, SQL

Streaming/Compute: Kafka (or Pulsar), Spark, Flink, Beam

Storage/OLAP: ClickHouse (primary), object storage (S3/GCS), Parquet/Iceberg/Delta

Orchestration/Workflow: Airflow, dbt (for transformations), Makefiles/Poetry/pipenv

ML/MLOps: MLflow/Weights & Biases, KServe/Seldon, Feast/custom feature store patterns, vector stores (optional)

Dashboards/BI: Superset (plugins, theming), Grafana for ops

Platform: Kubernetes, Docker, Terraform, GitHub Actions/GitLab CI, Prometheus/OpenTelemetry

Cloud: AWS/GCP/Azure

What we’re looking for

1. 4+ years building production data/ML or streaming systems with high TPS and large data volumes.
2. Strong coding skills in Python and one of Java/Scala;
   solid SQL and data modeling.
3. Hands‑on experience with Kafka (or similar), Spark/Flink, and OLAP stores—ideally ClickHouse.
4. GenAI pipelines: retrieval‑augmented generation (RAG), embeddings, prompt/tooling workflows, model evaluation at scale.
5. Proven experience designing feature pipelines with point‑in‑time correctness and backfills;
   understanding of online/offline consistency.
6. Experience instrumenting Superset dashboards tied to ClickHouse for operational and product analytics.
7. Fluency with CI/CD, containerization, Kubernetes, and infrastructure‑as‑code.
8. Solid grasp of distributed systems and architecture fundamentals: partitioning, consistency, idempotency, retries, batching vs. streaming, and cost/perf trade‑offs.
9. Excellent collaboration skills;
   ability to work cross‑functionally with DS/ML, product, and UI/UX.
10. Ability to pass a CodeSignal prescreen coding test.

Grid Dynamics (Nasdaq:GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, UK, Netherlands, Mexico, India, Central and Eastern Europe.

To learn more about Grid Dynamics, please visit . Follow us on Facebook , Twitter , and LinkedIn .

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About the Company:

UptimeAI is leading the way in predictive analytics and AI-driven solutions to optimize operational uptime and reduce downtime for industrial and enterprise clients. Our innovative platform harnesses cutting-edge data science to deliver actionable insights, ensuring maximum efficiency and reliability. UptimeAI uniquely combines Artificial Intelligence with Subject Matter Knowledge from 200+ years of cumulative experience to explain interrelations across upstream/downstream equipment, adapt to changes, identify problems, and give prescriptive diagnosis like a human expert would.

About the Role:

We're looking for a highly skilled and hands-on ML End-to-End Engineer. You'll need deep practical experience across the entire machine learning lifecycle, from data ingestion and model development to robust backend integration and scalable production deployment. We want someone who not only understands the theory but can demonstrate significant real-world application and problem-solving at every stage of ML product development.

Responsibilities:

• ML Model Development & Optimization:
• Algorithm Proficiency: Proven experience designing, training, and optimizing diverse ML models, including strong expertise in supervised learning, and significant practical experience with unsupervised learning (e.G., clustering, dimensionality reduction, anomaly detection) and reinforcement learning algorithms (e.G., Q-learning, policy gradients). You should be able to discuss specific challenges encountered during model development across these paradigms and how you resolved them.
• Frameworks: Hands-on expertise with PyTorch, TensorFlow, and scikit-learn.
• Libraries: Strong proficiency with NumPy, Pandas, SciPy, Seaborn, and Plotly for data manipulation, analysis, and visualization.
• Feature Engineering: Demonstrable experience in effective feature engineering, selection, and transformation techniques.
• Data Engineering & Management for ML:
• Data Pipelining: Proven experience building and managing robust data pipelines for ML, including data ingestion, cleaning, transformation, and validation.
• Database Proficiency: Strong command of SQL and NoSQL databases (e.G., PostgreSQL, MongoDB) for storing and retrieving data relevant to ML models.
• Real-time Data Streams: Expertise with Apache Kafka for building and managing real-time data ingestion and processing pipelines.
• Backend Development for ML Applications:
• API Development: Demonstrable experience designing, building, and maintaining RESTful APIs for serving ML model predictions.
• Programming Language & Frameworks: Strong proficiency in Python with practical experience using either Flask or FastAPI for backend service development.
• System Design: Ability to design scalable, fault-tolerant, and high-performance backend systems to support ML inference.
• MLOps & Production Deployment:
• Containerization: Expertise in containerization technologies (e.G., Docker) for packaging ML models and their dependencies.
• Orchestration: Experience with container orchestration tools (e.G., Kubernetes) for deploying and managing ML services at scale.
• CI/CD for ML: Proven ability to set up and manage CI/CD pipelines specifically for ML model training, testing, and deployment (e.G., Jenkins, GitLab CI, GitHub Actions).
• Monitoring & Logging: Experience in implementing robust monitoring, alerting, and logging solutions for production ML systems to ensure performance, reliability, and data drift detection
• Model Performance & Reliability:
• Performance Tuning: Proven ability to identify and resolve performance bottlenecks in ML models and backend services. This includes experience with fine-tuning models and applying techniques to extract maximum performance from them, such as quantization, pruning, or model compression.
• Model Versioning & Experiment Tracking: Experience with tools and practices for model versioning, experiment tracking (e.G., MLflow, DVC), and reproducibility.
• General Engineering & Problem Solving:
• Competitive Coding / Algorithmic Problem Solving: Demonstrated proficiency in competitive coding platforms (e.G., LeetCode, HackerRank, TopCoder, Codeforces) or a strong, demonstrable foundation in algorithms and data structures, showcasing exceptional problem-solving abilities.
• Security Best Practices for ML Systems: Understanding and implementation of security best practices for ML models, data, and APIs.

Qualifications:

• 5+ years of experience as ML Engineer in high-growth SaaS or product startups
• Strong problem-solving and engineering mindset, with a keen eye for scalability, reliability, and efficiency.
• Excellent communication skills for conveying complex technical information to both technical and non-technical stakeholders.
• Adaptable and enthusiastic about working in a fast-paced, product-driven environment.
• Proactive in learning new ML technologies, backend frameworks, and deployment methodologies
• Comfortable working in a fast-paced, ambiguous startup environment

Why to join UptimeAI:

• Impact Industry-Wide Change: Contribute to transformative solutions that significantly improve operational efficiency and reliability for global clients.
• Collaborative and Growth-Oriented Environment: Join a talented, passionate team that values innovation, continuous learning, and professional growth.
• Opportunities for Leadership and Innovation: Lead pioneering projects, influence product development, and shape the future of industrial AI solutions.

Pay range and compensation package:

(Pay range or salary or compensation)

Equal Opportunity Statement:

(Include a statement on commitment to diversity and inclusivity.)

As a Quantitative Machine Learning Engineer at Merli , you will help shape the next generation of AI-driven trading infrastructure . This role sits at the intersection of quantitative research, applied ML, and agentic system design , with the goal of optimizing high-frequency (HFT), medium-frequency (MFT), and wholesale trading strategies.

You’ll architect adaptive, agent-based ML systems that learn from evolving market microstructures, build robust forecasting and optimization models, and work with trading and infrastructure teams to deploy these solutions in real-world, low-latency environments .

Key Responsibilities

• Design & Build Agentic ML Systems: Develop autonomous and semi-autonomous agents that perform data acquisition, alpha discovery, backtesting, and execution optimization.
• End-to-End ML Engineering: Architect, train, and deploy ML pipelines for HFT/MFT and wholesale trading, from signal generation to execution integration.
• Quantitative Research Integration: Collaborate with quant researchers to translate theoretical models into production-ready predictive and optimization systems.
• Market Forecasting: Develop deep learning, time series, and reinforcement learning models for price movement prediction and regime detection.
• Trading Optimization: Build reinforcement and meta-learning frameworks that adaptively tune strategy parameters in live environments.
• Scalable ML Infrastructure: Implement real-time inference, model versioning, and continuous learning pipelines for production systems.
• Performance Evaluation: Rigorously validate models with historical and synthetic simulations, ensuring robustness, latency, and financial soundness.
• Documentation & Collaboration: Maintain high standards of reproducibility, version control, and code documentation across research and deployment layers.

What You’ll Gain

• Work at the frontier of AI, quantitative finance, and agentic automation .
• Collaborate with quant researchers, data engineers, and trading teams shaping next-gen trading systems.
• Exposure to meta-optimization frameworks , reinforcement learning , and multi-agent orchestration .
• Hands-on experience with low-latency ML deployment , GPU acceleration, and distributed training in real trading environments.
• Ownership of models that directly influence market-making, forecasting, and strategy execution .
• Continuous learning and experimentation in a research-first, innovation-driven environment.

Qualifications

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Applied Mathematics, Financial Engineering, or a related quantitative field.
• 3+ years of experience developing and deploying ML models in production (preferably in finance, trading, or large-scale decision systems).
• Strong proficiency in Python and ML frameworks: PyTorch, TensorFlow, scikit-learn, NumPy, Pandas .
• Deep understanding of supervised, unsupervised, and reinforcement learning , time-series modeling , and probabilistic forecasting .
• Experience building scalable data pipelines with Kafka, Flink, or Ray and deploying models in Docker/Kubernetes environments.
• Knowledge of market microstructure , portfolio optimization , or signal-based trading systems is highly desirable.
• Familiarity with meta-learning , agent-based system design , or multi-agent coordination is a strong plus.
• Solid analytical, programming, and debugging skills with an emphasis on system reliability and latency optimization .

Preferred Technical Stack

• Languages: Python, C++, Rust
• ML Infrastructure: Ray, MLflow, Airflow, Weights & Biases
• Data Systems: Kafka, Redpanda, Redis, QuestDB
• Model Deployment: Triton Inference Server, TorchServe, or custom GPU inference
• Cloud/Hybrid Setup: Kubernetes, ArgoCD, Helm

Job Title : Data Scientist /Senior Data Scientist-BFS

Location : Chennai, Bangalore, Hyderabad

Skills : Data Science, Model Building, Python, Regression, Classification

Domain : BFS

NP :Immediate to 15 Days

Who we are

Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.

Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence.

We are a Great Place to Work-Certified™ ), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine

Curious about the role? What your typical day would look like?

As a Data Scientist, your work is a combination of hands-on contribution to Loreum Ipsum, Loreum Ipsum, etc. More specifically, this will involve:

• Analytical Translation: Translate complex business problems into sophisticated analytical structures, conceptualising solutions anchored in statistical and machine learning methodologies.
• Problem Solving: While technical proficiency in data manipulation, statistical modelling, and machine learning is crucial, the ability to apply these skills to solve real-world business problems is equally vital.
• Client Engagement: Establish a deep understanding of clients' business contexts, working closely to unravel intricate challenges and opportunities.
• Algorithmic Expertise: Develop and refine algorithms and models, sculpting them into powerful tools to surmount intricate business challenges.
• Quantitative Mastery: Conduct in-depth quantitative analyses, navigating vast datasets to extract meaningful insights that drive informed decision-making.
• Cross-Functional Collaboration: Collaborate seamlessly with multiple teams, including Consulting and Engineering, fostering relationships with diverse stakeholders to meet deadlines and bring Analytical Solutions to life

What do we expect?

• 4 -10 years of Relevant Data Science Experience, with demonstrated proficiency and hands-on experience navigating data science complexities.
• Mandatory : Minimum 2+ years of experience in the Banking and Financial services industry
• Good communication skills, both verbal and written.
• Exhibit a fervour for crafting modular, scalable, and bug-free Python code.
• Comfortable in SQL with additional proficiency in office tools like Excel & PowerPoint.
• Experience in production engineering best practices (e.G. Git versioning, Docker).
• Familiarity or experience with working on large data sets and distributed computing (e.G. Hive, Hadoop, Spark)
• Working knowledge of Cloud platforms (e.G. AWS, Azure, GCP).
• Excitement to collaborate with diverse stakeholders across the organisation.
• In-depth understanding of various data science approaches, machine learning algorithms, and statistical methods.
• Hunger to learn new technologies and embrace the change.
• Proficiency in foundational concepts and algorithms in machine learning, encompassing regression and classification techniques, and a keen awareness of their assumptions, strengths, and limitations.
• Must-Have Skills: Regression/Classification/Optimization/ Python Proficiency in these key skills is crucial to thriving in this role.

You are important to us, let’s stay connected!

Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire.

Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry.

Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities

Role: AI Engineer

Experience- 3-5 years

Location- Mumbai/Gurugram/Bangalore/Coimbatore

Looking for candidates who can join us in Immediate to 15 days

Job Responsibility

• esSolve complex business problems by applying Large Language Model (LLM), Natural Language Processing (NLP), Computer Vision (CV), and Machine Learning (ML) techniqu
• esIntegrate AI algorithms into multiple product and solution offerin
• gsConduct a systematic study of AI research and industry advancements to apply insights to real-world business proble
• msResearch and advance the field of Generative AI, staying up to date with cutting-edge techniques and improving existing mode
• lsDevelop new models or optimize existing ones for practical applicatio
• nsBuild cognitive capabilities within multiple products and solution offerin
• gsEnvisage next-generation AI challenges and recommend solutions to address th
• emFacilitate the IP generation process and contribute toward the patenting of AI innovatio
• nsAdvance the field of Generative AI by researching state-of-the-art models (e.G., Transformers, BERT, GPT, T5,Gemini) and applying them to real-world use cas
• esOptimize AI models by working on word embeddings, attention mechanisms, and encoder-decoder architectur
• esImprove prompting strategies by deeply understanding LLM internals, rather than just applying surface-level prompt engineeri
• ngCollaborate with multi-functional teams to integrate AI-driven solutions into business applicatio

ns

Requirements & Qualifica

tionB.E/B.TECH/MS/M.TECH

/MCAYears of Experience: 3-5 y

ears

Skills & Compet

encies

Core AI & ML Exp

• erties.Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision,
• and NLPKnowledge of Generative AI models and Prompt Engi
• neeringAt least one production implementation of a GenAI
• projectKnowledge of the end-to-end deployment process of an AI
• projectHands-on Python programming (minimum 4
• years)Proficiency in tensorflow or

pytorch

Deep Learning Fund

• amentalsNeural Networks – CNNs, RNNs, Transformers, GANs, Attention Mechanisms & Self-Attention (as used in Transformers like BERT &a
• mp;
  GPT)Word Embeddings & Vector Representations (e.G., Word2Vec, Fast Text, BERT,
• GPT ,T5)LLM Fine-Tuning & Transfer Learning, RAG-based Archi

tectures

AI Model Optimization & En

• gineeringProficiency in PyTorch or TensorFlow for training and deploying deep learni
• ng modelsExperience with Vector Databases (e.G., FAISS, Pinecone) and
• LangChainGood understanding with cloud AI platforms like AWS, A
• zure, GCPUnderstanding of LLMOps & AI Model D

eployment

Preferred Skills (Bo

nus Points)Experien

ce in MLOpsPrevious research publications in peer-reviewed AI/ML journals or top conferences and pat

ents if a ny

Skills: - Artificial Intelligence, Machine Learning, Deep learning, Computer Vision, Natural Language Processing (NLP). Knowledge of various Generative AI model and Prompt Engineering. Hand-on coding skills in Python, at least 4 years python experience. (Preferred) Strong

NLP skills.