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 AIExperience: 12+ years overall, including 5+ years hands-on in production GenAI/Agentic AILocation: IndiaRole SummaryOwn 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 practiceRequired 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 stakeholdersPreferred 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 certificationsEducationBachelor's/Master's in Computer Science, Engineering, or related fieldQualification
15 years full time education