Tcs Hiring For Data Scientist

Job description

**Position: Gen AI Data Scientist** **Data Scientist and ML Engineer** **Skills: GenAI, LLM, AI Agents, RAG, Python** **Location: Chennai, Hyderabad, Bangalore, Mumbai, Gurgaon, Pune, Kolkata** **Role- Data Scientist** - 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, DeepEval - Create Conversational Interface with React JS or other Frontend components, Develop and deploy AI agents using LangGraph and ADK, A2A, MCP - Strong 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 strategies - Knowledge of API Gateways and ISTIO , ability to Diagnose and intercept failures in End to End communication - Hands-on Experience with API Development and Microservices architecture **Desirable 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 Integrations - Experience 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.