Senior AI Lead - Azure AI Foundry

Senior AI Lead - Azure AI Foundry

Job type-Contractual - Full Time

Job location- Bangalore

Duration (months) - 6

Total experience (yrs)- 10+ years

Mandatory Skills

GenAI (3-5 Year)

multi-agent architectures (5-8 Year)

Architecture (5-8 Year)

Agentic AI (3-5 Year)

Python (8-10 years)

Azure AI Foundry (8-10 years)

Azure OpenAI (8-10 years)

Azure AI Search (8-10 years)

Azure AI Agent Service (8-10 years)

AI Engineering Controls (8-10 years)

Role/Job Description

• Role Senior AI Lead Agentic & Gen AI - Azure AI Foundry
• Experience 15+ years overall; 5+ years Architecture; 3-5+ years MANDATORY AZURE AI FOUNDRY, 3+ years hands-on GenAI / Agentic AI
• Primary Focus Production-scale Agentic AI, Generative AI, multi-agent architectures and enterprise AI platforms
• Cloud AWS / Azure / GCP experience with at least one cloud platform is mandatory

1. Role Overview

• We are seeking a Senior AI Lead - AZURE DATA FOUNDRY to own an Agentic AI engineering POD and lead the design, development and deployment of enterprise-grade Generative AI and multi-agent solutions. The role will translate functional requirements into technical workstreams, drive delivery across engineering PODs, contribute hands-on code, establish AI engineering standards, and mentor development teams.

2. Key Responsibilities

• Own the Agentic AI POD and deliver identified features in collaboration with Data, UI/UX, Infrastructure and other
• PODs.
• Translate functional requirements into technical tasks, architecture decisions and executable delivery plans;
• drive the POD through implementation.
• Design and develop multi-agent solutions using Azure AI Foundry and Azure AI Agent Service.
• Build AI agents and implement agent orchestration, workflow management, memory and state-management
• components.
• Design and implement RAG solutions using Azure AI Search and vector stores.
• Develop prompt-engineering, tool-calling, function-calling and agent-collaboration frameworks.
• Implement observability, distributed tracing, evaluation and AgentOps capabilities.
• Develop Human-in-the-Loop workflows and agent recovery mechanisms.
• Contribute directly to code development, conduct code reviews and mentor development teams.
• Troubleshoot performance, response quality, hallucination, model/agent drift and orchestration issues.
• Define and implement AI governance, security and compliance controls.
• Lead development and deployment of production-grade Generative AI solutions.

3. Mandatory Requirements Non-Negotiable

Candidates who do not meet ANY one of the following mandatory requirements should be rejected at screening

stage.

1. Experience: 15+ years overall professional experience AND 5+ years hands-on Architecture experience.
2. GenAI / Agentic AI : 3+ years hands-on development experience in Generative AI and/or Agentic AI.
3. Enterprise AI: Demonstrated development AND deployment of enterprise grade production GenAI solutions.
4. Python: Strong hands-on expertise in Python.
5. Azure AI Foundry: Hands-on project experience using Azure AI Foundry.
6. Azure OpenAI: Hands-on project experience integrating/deploying LLM solutions using Azure OpenAI.
7. Azure AI Search: Hands-on experience implementing RAG/search solutions using Azure AI Search.
8. Azure AI Agent Service: Hands-on experience with Azure AI Agent Service.
9. Multi-Agent Architecture: Production-scale multi-agent systems including orchestration, workflow, memory and state management.
10. AI Engineering Controls: Hands-on governance, security, observability, evaluation and Human-in-the-Loop capabilities.
11. Cloud: Hands-on exposure to AWS, Azure or GCP.
12. Education: MS/M.Tech in CS, Information Systems, Computer Engineering or closely relevant discipline, or equivalent relevant degree.

Evidence rule: mandatory skills must be demonstrable through actual employment/project experience. Skills

appearing only in a profile summary, keyword/skills section or certification list are not sufficient evidence. Failure on

any mandatory criterion = REJECT.

4. Required Skills

• AI / GenAI (MUST HAVE) : Generative AI, LLM applications, Agentic AI, multi-agent systems, RAG, prompt engineering, tool calling and function calling.
• Microsoft AI Stack (MUST HAVE) : Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure AI Agent Service, Document Intelligence and Azure AI services.
• AI Frameworks : Semantic Kernel, LangGraph, LangChain, AutoGen, MCP; practical agent orchestration and collaboration.
• Agent Engineering : Agent building, orchestration, workflow management, memory, state management, recovery and Human-in-the-Loop.
• AI Quality & Operations : Observability, tracing, evaluation, AgentOps, hallucination mitigation, performance/quality troubleshooting and drift management.
• Architecture : REST APIs, microservices, event-driven architecture, distributed systems and scalable enterprise architecture.
• Data & Storage : PostgreSQL, Cosmos DB, Redis, vector stores and enterprise retrieval architectures.
• Cloud & Deployment : Azure strongly preferred; AWS/GCP acceptable for the cloud requirement. Production AI deployment is essential.
• Engineering Practices : Code development, code reviews, technical documentation, Agile delivery and mentoring.
• Governance & Security : AI governance, security, compliance, responsible AI controls and enterprise deployment standards.

5. Qualifications

• Postgraduate degree (MS/M.Tech) in Computer Science, Information Systems, Computer Engineering or closely relevant discipline, or equivalent relevant qualification.
• 15+ years overall professional experience.
• 5+ years Architecture experience.
• 3+ years hands-on Generative AI / Agentic AI development.

6. Ideal Candidate Profile

• The ideal candidate is a hands-on AI architect/technical lead combining deep software architecture capability with recent production GenAI and Agentic AI engineering. They should be able to move from functional requirements to technical architecture, guide an engineering POD, make pragmatic platform choices, and personally contribute to implementation while ensuring enterprise-grade security, governance, observability and AI quality.