Data Scientist

AI Architect – Generative AI & Agentic Systems

Domain: Healthcare Preferred

Location: Noida (Work from Office – 2 to 3 Days/Week)

Band: D1 / D2

10 Years in Data science-Gen AI-3 Plus years

Team: Analytics & AI / Workforce Solutions

The Opportunity

We are looking for a hands-on technical leader to help shape the future of enterprise AI. This role goes beyond traditional chatbot implementations and focuses on building autonomous agentic systems and advanced reasoning frameworks.

You will lead the design of next-generation AI architectures combining Graph RAG, Temporal Knowledge Graphs (TKG), and Multi-Agent Orchestration to solve complex workforce and operational challenges. The role requires close collaboration with business stakeholders, client leadership, and engineering teams to deliver scalable, state-of-the-art AI solutions.

Key Responsibilities

1. Advanced GenAI Architecture & Innovation

• Design and deploy multi-agent AI systems capable of planning, reasoning, and autonomous task execution using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestrators.
• Build advanced RAG architectures including Graph RAG and hybrid retrieval pipelines that combine semantic retrieval with structured knowledge systems to improve accuracy and reduce hallucinations.
• Lead development of Temporal Knowledge Graphs (TKG) to model evolving relationships and time-aware contextual intelligence.
• Ensure production-grade deployment standards covering latency optimization, scalability, semantic caching, evaluation frameworks (RAGAS, TruLens), observability, and AI guardrails.
• Define best practices for enterprise LLM architecture, orchestration, memory handling, and tool usage.

2. Strategic Technical Leadership

• Translate complex and ambiguous business problems into scalable AI solution roadmaps.
• Partner with client stakeholders and leadership teams to demonstrate the business value of Agentic AI solutions across areas such as claims processing, audit automation, workforce planning, and operational intelligence.
• Act as the technical authority during solution discussions and client engagements, confidently defending architecture decisions and trade-offs.
• Lead rapid prototyping and MVP development initiatives and drive successful transition into enterprise production environments.

3. Collaboration & Ecosystem Growth

• Drive adoption of modern AI infrastructure including Vector Databases (Pinecone, Weaviate), Graph Databases (Neo4j), and LLMOps tooling.
• Mentor Data Scientists, ML Engineers, and AI practitioners on advanced GenAI concepts including prompt engineering, fine-tuning strategies, RAG optimization, agent orchestration, and evaluation techniques.
• Contribute to internal innovation initiatives and external thought leadership in areas such as Graph RAG, Knowledge Graphs, and Agentic AI systems.

What We Are Looking For

Must-Have Skills & Experience

• Strong engineering background in Software Engineering, AI Engineering, or ML Engineering.
• Hands-on expertise with advanced RAG architectures including Graph RAG and hybrid retrieval systems.
• Experience building or prototyping Agentic AI systems using frameworks such as LangChain Agents, LangGraph, AutoGen, CrewAI, or equivalent.
• Strong understanding of Knowledge Graphs, ontologies, and Temporal Knowledge Graph concepts.
• Practical experience designing scalable and production-ready GenAI systems with strong focus on reliability, evaluation, observability, and cost optimization.
• Ability to bridge technical depth with business impact and translate AI capabilities into measurable outcomes.

If you want, I can also:

• make it more LinkedIn post ready
• shorten it into a JD flyer
• or make it more senior/executive sounding for CTO-level hiring campaigns