We are looking for an experienced Data Scientist with expertise in Generative AI (GenAI), Agentic AI, and Machine Learning to design, develop, and deploy scalable, production-ready AI solutions. The ideal candidate should possess strong hands-on technical skills, proven client-facing experience, and the ability to lead AI/ML projects from solution design through production deployment.

Key Responsibilities & Requirements :

Technical Leadership :

- Independently engage with clients, understand business challenges, translate requirements into AI/ML solutions, and drive technical decisions.

GenAI & Agentic AI :

- Strong hands-on experience with RAG, LangChain, LangGraph, LLM orchestration, tool/function calling, multi-agent workflows, prompt/context engineering, vector databases, and major LLM APIs.

Production-Grade AI Systems :

- Build robust agent/LLM harnesses incorporating state management, tool execution, retries/fallbacks, guardrails, observability, and model/provider abstraction.

GenAI Evaluation :

- Experience with LangSmith, RAGAS, DeepEval, Arize Phoenix, Promptfoo, or equivalent frameworks to evaluate retrieval quality, groundedness, hallucinations, tool usage, agent trajectories, latency, and cost.

Machine Learning :

- Strong foundation in predictive modeling, including regression, classification, clustering, feature engineering, model selection, model validation, NLP, embeddings, and transformer-based models.

Python Development :

- Minimum 5+ years of hands-on Python experience, with expertise in API development, asynchronous programming, testing, modular design, Git, and frameworks such as FastAPI.

Cloud & MLOps/LLMOps :

- Experience deploying and operating AI/ML solutions using cloud platforms, Docker, CI/CD, monitoring, and scalable production architectures.

AI Security & Responsible AI :

- Understanding of prompt injection, data privacy, access controls, hallucination mitigation, secure tool execution, and enterprise AI governance.

Technical Skills :

- GenAI/Agentic AI : RAG, LangChain, LangGraph, LLM APIs, multi-agent systems, vector databases

- Evaluation Frameworks : LangSmith, RAGAS, DeepEval, Arize Phoenix, Promptfoo

- Machine Learning : scikit-learn, PyTorch, TensorFlow, NLP, embeddings, transformers

- Programming : Python, FastAPI, REST APIs, asynchronous programming, Git

- Deployment : Cloud platforms, Docker, CI/CD, MLOps, LLMOps, monitoring

Ideal Candidate Profile :

A hands-on AI professional with strong problem-solving and software engineering skills, capable of leading technical discussions, working directly with clients, and delivering secure, reliable, and scalable AI solutions in production environments.