Data Science Practitioner

Project Role : Data Science Practitioner

Project Role Description : Formulating, design and deliver AI/ML-based decision-making frameworks and models for business outcomes. Measure and justify AI/ML based solution values.

Must have skills : Data Science

Good to have skills : NA

Minimum 12 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

AI Engineering Lead / Solution Architect – Knowledge Graph & Agentic AI

Experience: 12+ years overall, including 5+ years hands-on in production GenAI/Agentic AI

Location: India

Role Summary

Own enterprise AI architecture and platform strategy for agentic, multi-agent, and knowledge-graph-backed systems. Lead the full AI lifecycle — use-case discovery, architecture, engineering, governance, security, and production optimization — while mentoring engineering teams and representing architecture decisions to senior stakeholders.

Key Responsibilities

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, low-latency enterprise workloads

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

Own responsible-AI, security (RBAC, SSO, audit logging, encryption), and compliance posture for regulated-industry deployments

Mentor AI engineers/architects on agentic AI, RAG, and cloud AI engineering practices

Partner with client/executive stakeholders to translate business priorities into an AI delivery roadmap

Champion technical innovation and reusable frameworks across the AI engineering practice

Required Skills & Experience

12+ years in software/AI engineering, with 5+ years hands-on in production GenAI, RAG, and agentic/multi-agent systems

LLM engineering: GPT/Claude/LLaMA-class models, prompt engineering, function/tool calling, RAG architecture

Agentic AI frameworks (e.g., CrewAI, LangGraph, or equivalent) working knowledge of agent-to-agent and tool-calling protocols (e.g., MCP, A2A)

Hands-on depth with Azure AI and integration services — Azure AI Foundry, Azure Functions, Azure Logic Apps — or equivalent AWS/GCP AI and serverless stacks

Knowledge graph / semantic technologies — Neo4j or equivalent, graph-based retrieval and ontology patterns

Strong programming: Python, TypeScript/Node.js, FastAPI, React.js, SQL

Data layer: PostgreSQL, MongoDB, and cloud-native relational/NoSQL stores, plus vector/AI-search indexing

AI evaluation and observability practices for production LLM systems

Track record owning enterprise architecture, security, and governance for regulated industries

Experience mentoring engineers and presenting to executive stakeholders

Preferred

Hands-on experience with Neo4j (or an equivalent graph database) and knowledge graph / ontology design for enterprise semantic layers

Production deployment experience across multiple major cloud platforms (AWS, Azure, and GCP) — spanning AI/ML services, managed databases, and serverless compute

Experience integrating real-time conversational voice or avatar interfaces into enterprise applications

Familiarity with no-code/low-code AI platforms (e.g., Microsoft Power Platform, Copilot Studio, or equivalent) and how they fit into an enterprise agentic AI strategy

Relevant cloud architecture or graph-database certifications

Education

Bachelor's/Master's in Computer Science, Engineering, or related field