Data Scientist ML Engineer

Role & responsibilities\n\nLocation - CHN, HYD, BLR, MUM, GGN, PUNE\n\nInterested and available candidates on 13-May-26 for Virtual interview can share profile.\n\nRole- Data Scientist ( 5 to 12 Yrs )\nHands-on experience with GenAI, Gemini or Open source LLMs and develop GenAI applications for Code Translation, Text Extraction, Summarisation and SDLC Optimization etc.\nHands-on Experience with AI Agents, Chat bots, RAG (Retrieval-Augmented Generation), and vector databases. ( PG vector / croma DB )\nHands-on Experience with GenAI Performance Evaluation tools like Pegasus, Ragas, DeepEval\nCreate Conversational Interface with React JS or other Frontend components, Develop and deploy AI agents using LangGraph and ADK, A2A, MCP\nStrong programming skills in Python (experience with LangChain/LangGraph / LangSmith frameworks) and TypeScript ( preferable )\nSolid understanding of LLMs, prompt engineering, and graph-based workflows.\nKnowledge and implementation of Input and Output guardrails in addressing Hallucination, PII filtering, HAP and Bias etc.\nImplemented security best practices, Experience to address spikes and Denial of wallet attacks, DDoS attack and other Spike arrest strategies\nKnowledge of API Gateways and ISTIO , ability to Diagnose and intercept failures in End to End communication\nHands-on Experience with API Development and Microservices architecture\nDesirable skills/knowledge/experience: (As applicable)\nStrong experience applying machine learning, statistical modelling, and predictive analytics to real‑world business problems.\nCollaborate with cross-functional teams to ability to resolve end to end connectivity and Data Integrations\nExperience working with large, complex datasets, including data cleaning, feature engineering, and exploratory data analysis.\nFamiliarity with LLMs, NLP techniques, and GenAI frameworks, including embeddings, prompt engineering, or fine‑tuning.\nExperience building end‑to‑end ML pipelines, including model validation, optimisation, deployment, and monitoring.\nUnderstanding of MLOps practices, including model versioning, model registries, CI/CD for ML, and automated training/inference workflows.\nAbility to translate business problems into analytical tasks and communicate insights in a clear, concise manner to technical and non‑technical audiences.\nKnowledge of data governance, including data quality, lineage, ethics, privacy considerations, and responsible AI principles.\nComfort working with cloud platforms (GCP preferred) for model training, deployment, and scalable compute.\nA growth‑oriented mindset with enthusiasm for exploring new algorithms, tools, and emerging AI/ML techniques.