Artificial Intelligence Engineer

Trantor; Bengaluru; Presencial; INR 4.000.000 - 9.000.000; Jornada completa

Descripción de la vacante

Trantor in Bengaluru, India is seeking an experienced AI engineer to join the core AI engineering team and build production GenAI features for our insurance technology platform.

You will design, implement, evaluate and scale LLM-based solutions, develop agentic workflows, automate document processing and integrate with backend services. The role emphasizes strong Python and GenAI foundations with Azure OpenAI and related tooling.

Formación

• Proven experience delivering GenAI/LLM solutions in production.
• Strong Python delivery of AI/ML applications.
• Experience with AI agents, function calling and tool orchestration.
• Experience with Azure AI stack and related tooling.

Responsabilidades

• Build GenAI solutions for insurance; ship production-grade LLM features.
• Develop agentic workflows with tool calling and orchestration.
• Automate document classification, data extraction and summarization.
• Assess models and routing strategies balancing quality, latency and cost.
• Set up evaluation suites, outputs and guardrails; run regression tests.
• Tune for latency, token usage and scale; monitor production systems.
• Integrate AI services with backend APIs and enterprise workflows.
• Mentor engineers and evaluate new models/frameworks.

Conocimientos

Python

GenAI

LLMs

Agents

Azure AI

Quality engineering

Backend APIs

Docker

CI/CD

AI observability

Herramientas

Azure OpenAI

Microsoft Foundry

LangGraph

Semantic Kernel

Claude Code

GitHub Copilot

PostgreSQL

Descripción del empleo

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 summarization.

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:

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.