GenAI Data scientist

Greetings from TCS!!Role: Gen AI Data ScientistData Scientist and ML EngineerSkills: GenAI, LLM, AI Agents, RAG, Python

Location: Chennai, Hyderabad, Bangalore, Mumbai, Gurgaon, PUNE, Kolkata.Preferred candidate profile :Hands-on experience with GenAI, Gemini or Open source LLMs and develop GenAI applications for Code Translation, Text Extraction, Summarisation and SDLC Optimization etc.Hands-on Experience with AI Agents, Chat bots, RAG (Retrieval-Augmented Generation), and vector databases. ( PG vector / croma DB )Hands-on Experience with GenAI Performance Evaluation tools like Pegasus, Ragas, DeepEvalCreate Conversational Interface with React JS or other Frontend components, Develop and deploy AI agents using LangGraph and ADK, A2A, MCPStrong programming skills in Python (experience with LangChain/LangGraph / LangSmith frameworks) and TypeScript ( preferable )Solid understanding of LLMs, prompt engineering, and graph-based workflows.Knowledge and implementation of Input and Output guardrails in addressing Hallucination, PII filtering, HAP and Bias etc.Implemented security best practices, Experience to address spikes and Denial of wallet attacks, DDoS attack and other Spike arrest strategiesKnowledge of API Gateways and ISTIO , ability to Diagnose and intercept failures in End to End communicationHands-on Experience with API Development and Microservices architectureDesirable skills/knowledge/experience: (As applicable)Strong experience applying machine learning, statistical modelling, and predictive analytics to realworld business problems.Collaborate with cross-functional teams to ability to resolve end to end connectivity and Data IntegrationsExperience working with large, complex datasets, including data cleaning, feature engineering, and exploratory data analysis.Familiarity with LLMs, NLP techniques, and GenAI frameworks, including embeddings, prompt engineering, or finetuning.Experience building endtoend ML pipelines, including model validation, optimisation, deployment, and monitoring.Understanding of MLOps practices, including model versioning, model registries, CI/CD for ML, and automated training/inference workflows.Ability to translate business problems into analytical tasks and communicate insights in a clear, concise manner to technical and nontechnical audiences.Knowledge of data governance, including data quality, lineage, ethics, privacy considerations, and responsible AI principles.Comfort working with cloud platforms (GCP preferred) for model training, deployment, and scalable compute.A growthoriented mindset with enthusiasm for exploring new algorithms, tools, and emerging AI/ML techniques.