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

Neoveda Technologies; Bengaluru; Vor Ort; INR 1.800.000 - 2.800.000; Vollzeit

Zusammenfassung

Neoveda Technologies in Bengaluru is seeking a Lead AI Engineer to design, build and scale production AI using GenAI, LLMs, agentic workflows and intelligent document processing for our insurance technology platform.

You will lead the team, mentor engineers, and drive delivery of production-grade AI features across underwriting, claims and policy-servicing with focus on latency, reliability, and maintainability.

Qualifikationen

• Hands-on Python and AI/ML delivery.
• Experience with GenAI foundations and agents.
• Strong design for scalable AI systems.
• Knowledge of Azure AI stack and OpenAI/Claude-like models.

Aufgaben

• Build GenAI solutions for insurance including production-grade LLM features.
• Create agentic workflows with tool calling and orchestration.
• Automate document processing: classification, extraction, summarization.
• Evaluate models and routing strategies to balance quality and cost.
• Measure quality with evaluation suites, guardrails and regression tests.
• Tune latency and monitor production systems; perform root-cause analysis.
• Integrate AI services with backend APIs and enterprise workflows.
• Lead and mentor AI engineers; set engineering standards.

Kenntnisse

Python

GenAI & LLMs

Agent workflows

Quality engineering

Azure AI stack

Agent frameworks

Foundation models

Backend APIs

Tools

Git

Docker

CI/CD

Azure DevOps

GitHub Actions

Jobbeschreibung

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

• 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.

Experience we are looking for

• 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.