Lead AI Engineer

Neoveda Technologies; Bengaluru; On-site; INR 4,200,000 - 6,800,000; Full time

Job summary

Neoveda Technologies in Bengaluru is seeking a Lead AI Engineer to design and deliver GenAI‑enabled insurance solutions from concept to production. You will lead a team of AI engineers, collaborate with Core Engineering and Solutions teams, and own performance, reliability and governance of AI systems.

The role requires 6–10 years in AI/ML with hands‑on delivery and deep knowledge of Azure AI tools, LLMs, and enterprise workflows.

Qualifications

• 6–10 years in AI/ML engineering with GenAI/LLM work.
• Proven production delivery from experimentation to deployment and monitoring.
• Strong grounding in system design, scalability and maintainability.
• Insurance domain experience is a plus.

Responsibilities

• Build GenAI solutions for insurance, shipping production‑grade LLM features.
• Create agentic workflows with tool calling and orchestration.
• Automate document work: classification, data extraction and summarisation.
• Evaluate models and routing strategies balancing quality, latency and cost.
• Tune, operate and monitor production systems, with root‑cause analysis.
• Integrate AI services with backend APIs, databases and workflows.
• Lead the team, mentor engineers and evaluate new models.

Skills

AI/ML engineering

GenAI work

Production delivery

System design

Insurance domain knowledge

Tools

Azure AI stack

Azure OpenAI

LangGraph

Semantic Kernel

Claude Code

Job description

Role: Lead AI Engineer

Location: Bangalore (work from office)

Duration: Full time (Permanent)

Working Hours: 11:00 AM to 8:00 PM IST

Notice Period: Immediate joiners preferred; maximum 20-30 days

Must Have: Insurance domain experience

Required Skills:

• 6-10 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.

What you will do:

• 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

• 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.
• 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.