Data Scientist - GenAI / RAG

• Build and deploy RAG (Retrieval-Augmented Generation) systems & AI chat interfaces
• Work closely with client data science teams (ML/DL ecosystems)
• Develop GenAI-based enterprise knowledge solutions
• Collaborate directly with stakeholders and customers

Focus: We need candidates with real hands-on experience in Agentic AI and Traditional Data Science, not candidates who have only recently started exploring GenAI/LLMs. Continuous production implementation experience is preferred., * Candidates who can discuss end-to-end problem solving, feature engineering, predictive analytics, model selection, and solution design.

• Hands-on experience designing and implementing RAG, LLM, and Agentic AI solutions.
• Strong understanding of business use cases and ability to interact with customers and stakeholders.
• Ability to explain architecture decisions, technical trade-offs, and implementation approaches.

Mandatory Skills

• Strong Data Science background
• Strong Machine Learning implementation experience
• Hands-on LLM & RAG architecture and implementation
• Experience with Agentic AI concepts and workflows
• Strong client-facing / consulting and stakeholder communication skills
• Exposure to Deep Learning concepts and implementations
• Experience with cloud AI platforms such as AWS Bedrock, Azure OpenAI, or Vertex AI, * Strong hands-on experience in Agentic AI and Multi-Agent Systems
• Experience with LangGraph, LangChain, MCP (Model Context Protocol), tool calling, agent orchestration
• Strong RAG implementation experience including Vector Databases, Hybrid Retrieval, Reranking, Knowledge Graphs
• Hands-on experience with AWS Bedrock and enterprise GenAI solutions
• Strong Python and SQL skills
• Solid Traditional Data Science / Machine Learning background:
• Classification
• Regression
• Forecasting
• Anomaly Detection
• Feature Engineering
• Model Evaluation
• Experience with XGBoost, CatBoost, Random Forest, Deep Learning frameworks (PyTorch/TensorFlow)
• Experience designing and deploying production AI/ML systems
• Understanding of MLOps / LLMOps, model monitoring, evaluation, observability, and retraining pipelines
• Ability to translate business problems into ML or Agentic AI solutions
• Experience with LLM Evaluation, Hallucination Detection, Groundedness and Retrieval Quality metrics
• Exposure to Databricks, Spark, Vector Databases, APIs, Cloud Platforms (AWS preferred)