Artificial Intelligence Engineer

Job Description

AI Technical Architect / Engineering Lead (Generative AI & Agentic Systems) ? Experience: 8?14 years overall; 2?4 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 stack?from 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 systems?ingestion, 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 3?8 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.