Opportunity: Lead / Senior AI Engineer (GenAI & Agentic AI) Bangalore

Lead AI Engineer
Total Experience : 6+ years
Notice period : serving Up to 20-30 days (only serving candidate)
Location : Bangalore (work from office )
Shift timing : General (11 AM to 8 PM)
Please find IAS Sheet
S No Skill Area
Interview Focus (What to Evaluate)
Priority
610 years in AI/ML/applied AI/software engineering with
substantial GenAI/LLM experience
1
AI Engineering Experience
Must Have
2
3
Python & Applied AI
LLMs & GenAI
Strong hands-on Python and AI/ML application development
Production-grade LLM/GenAI solution development
Must Have
Must Have
AI agents, tool/function calling, orchestration and multi-step
workflows
4
5
6
AI Agents & Function Calling
Document AI
Must Have
Must Have
Must Have
Document classification, extraction and summarization
Prompt design, structured outputs, evaluation, guardrails and
AI regression testing
Prompting, Evaluation & Guardrails
Monitoring, latency/token optimization, quality and
root-cause analysis
7
8
9
AI Observability & Optimization
Azure AI Stack
Must Have
Must Have
Must Have
Azure AI, Microsoft Foundry and Azure OpenAI
Microsoft Agent Framework, LangGraph, Semantic Kernel or
similar
Agent Frameworks
10
11
12
Foundation Models
Backend & Database
DevOps & Deployment
OpenAI, Anthropic Claude or comparable models
REST APIs, asynchronous processing and PostgreSQL
Git, Docker, CI/CD, Azure DevOps / GitHub Actions
Must Have
Must Have
Must Have
Taking AI solutions from prototype/evaluation through
production deployment and monitoring
Scalable, maintainable AI system design and analytical
problem solving
13
14
Production AI Delivery
Must Have
Must Have
System Design & Scalability
15
11
AI Leadership
Mentoring AI engineers and defining engineering standards
Must Have
Must Have
AI-Assisted Development
Claude Code, GitHub Copilot or similar tools
Enterprise workflow experience and insurance domain
knowledge
16
Enterprise & Insurance
Must Have
Job Description
About the role

Join our core AI engineering team to build the intelligence behind our insurance technology platform.
You will design, build, evaluate and scale production AI using generative AI, LLMs, agentic workflows and
intelligent document processing, turning insurance business problems into dependable, measurable
capabilities.
What you will do



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Build GenAI solutions for insurance. Design and ship production-grade LLM and generative AI
features that solve real underwriting, claims and policy-servicing problems.
Create agentic workflows. Develop AI agents with tool calling, orchestration and multi-step
reasoning that can run reliably inside business processes.
Automate document work. Build capabilities for document classification, data extraction and
summarisation.
Choose the right model. Assess various models from Azure AI define routing strategies that
balance quality, latency, reliability and cost.
Measure quality. Set up evaluation suites, structured outputs and guardrails, and run automated
regression tests so quality stays visible as the product changes.
Tune and operate. Optimise for latency, token usage and scale; monitor production systems and
lead root-cause analysis when quality or performance slips.
Integrate and collaborate. Connect AI services to backend APIs, databases and enterprise
workflows alongside Core Engineering, Product and Solutions teams.
Lead the team. Mentor AI engineers, shape engineering standards, and keep evaluating new
models and frameworks for fit.
What you will bring
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Python and applied AI: strong hands-on delivery of AI/ML applications.
GenAI foundations: LLMs, agents and function calling.
Quality engineering: prompt design, structured outputs, guardrails, evaluation and AI
observability.
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Azure AI stack: Azure AI, Microsoft Foundry and Azure OpenAI.
Agent frameworks: Microsoft Agent Framework, LangGraph, Semantic Kernel or similar.
Foundation models: working experience with OpenAI, Anthropic Claude or comparable providers.
Backend skills: REST APIs, asynchronous processing, PostgreSQL (vector search is a plus).
Delivery tooling: Git, Docker, CI/CD with Azure DevOps or GitHub Actions, and AI-assisted
development tools such as Claude Code and GitHub Copilot.
Experience we are looking for

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610 years in AI engineering, ML engineering, applied AI, or software engineering with substantial
GenAI/LLM work.
Proven production delivery: you have taken AI solutions from experiment and evaluation through
deployment and monitoring, beyond prototypes.
Engineering depth: solid grounding in system design, scalability and maintainability, with sharp
analytical problem-solving.
Nice to have: enterprise workflow experience and insurance domain knowledge.