Artificial Intelligence Engineer

AI Technical Architect / Engineering Lead (Generative AI & Agentic

Systems)

Experience: 814 years overall; 24 years hands-on with GenAI/LLMs

Location: Pune (Hybrid)

Function: Architecture + Hands-on Engineering Leadership

About the role

You will lead the architecture, delivery, and evolution of production-grade AI systems with a

primary focus on Generative AI and agentic workflows. You'll guide a high-impact

team across the full stackfrom data and infrastructure to APIs and front-end

ensuring performance, safety, and reliability at scale.

What you'll do

1. Own AI architecture: Design end-to-end systemsingestion, retrieval,

agentic reasoning (planning, memory, tool use), guardrails, evaluation,

and observability.

2. Deliver agentic workflows: Build multi-agent, tool-augmented systems with robust

orchestration, fallback, human-in-the-loop, and recovery patterns.

3. GenAI solutions: Implement RAG, structured outputs, function/tool calling;

combine LLMs with deterministic microservices and domain logic.

4. Full-stack leadership: Oversee backend services (Python/Java), APIs/middleware, and

modern web front-ends (React/Angular/Vue) for AI-powered UX.

5. Data & retrieval: Architect hybrid retrieval (vector + keyword + rerankers), schema

design, chunking strategies, embeddings, and freshness policies.

6. LLMOps/MLOps: Know-How of establishing CI/CD for prompts/models,

offline/online evals, canaries, rollback, and cost/latency budgets.

7. Cloud & platform: Deploy and scale on Azure/AWS using Docker/Kubernetes; define

IaC, networking, secrets, and runtime policies.

8. Security & governance: Implement defenses for prompt injection, data leakage/PII,

toxicity; enforce RBAC/ABAC, auditability, and compliance.

9. Engineering excellence: Champion coding standards, testing/eval culture,

performance profiling, and production SLOs/SLAs.

10. Team & stakeholders: Lead a 38 engineer pod; mentor developers; partner with

PM/Design; translate business goals into technical roadmaps.

11. Platform stewardship: Champion and evolve our internal Purple Fabric AI platform;

drive adoption and integration across products.

12. Delivery practices: Run Agile rituals; manage scope, risks, and timelines using

JIRA and Confluence.

Must-have skills

1. Shipped AI systems: Track record of 2+ production LLM/GenAI solutions with real

users and measurable impact.

2. GenAI depth: RAG, tool/function calling, prompt engineering, structured outputs,

grounding strategies, LangChain/LangGraph/LlamaIndex and hallucination mitigation.