Neoveda Technologies
Lead AI Engineer
- Compensation
- ₹2.5M–₹3M / yr
- Location
- Bangarapet; Bangalore Rural; Bengaluru
- Work setup
- Onsite
- Seniority
- Lead
- Employment
- Full time
- Experience
- 6+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 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.
Full description
Role: Lead AI EngineerLocation: Bangalore (work from office)Duration: Full time (Permanent)Working Hours: 11:00 AM to 8:00 PM ISTNotice 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.