Data Scientist ML Engineer

Role & responsibilities

Location - CHN, HYD, BLR, MUM, GGN, PUNE

Interested and available candidates on 13-May-26 for Virtual interview can share profile.

Role- Data Scientist ( 5 to 12 Yrs )

• 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 real‑world 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 fine‑tuning.
• Experience building end‑to‑end 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 non‑technical 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 growth‑oriented mindset with enthusiasm for exploring new algorithms, tools, and emerging AI/ML techniques.