Data Science Practitioner

About the role

Formulating, design and deliver AI/ML-based decision-making frameworks and models for business outcomes. Measure and justify AI/ML based solution values. Formulating, design and deliver AI/ML-based decision-making frameworks and models for business outcomes. AI Engineering Lead / Solution Architect – Knowledge Graph & Agentic AI

What you'll do

• Define enterprise AI/agentic architecture standards, security, governance, and observability frameworks across engagements
• Lead design reviews for GenAI, RAG, and multi-agent solutions, from high-level architecture through detailed design
• Architect and oversee production-grade agentic / multi-agent AI platforms (e.g., CrewAI, LangGraph, or equivalent orchestration frameworks) supporting high-concurrency
• Design and integrate knowledge graph / semantic layer components (e.g., Neo4j or equivalent) for retrieval, reasoning, and data grounding across agentic workflows
• Build reusable AI platform capabilities — LLMOps, model evaluation, and observability practices — and drive enterprise adoption roadmaps

What you need

• 12–22 years in data science & ai
• Machine learning
• SQL
• LLMs
• Python
• AWS
• Prompt engineering
• M.Sc or B.Tech or B.E. or B.Sc or BCA or M.Tech or MCA

Good to have

• Azure
• GCP
• Node.js
• FastAPI
• PostgreSQL
• MongoDB

What you get