Product Engineering Lead — AI & Agent Platform Tabhi Inc. — Product Engineering — Job Title: Product Engineering Lead — AI & Agent Platform Posting Title: AI Architect — Agent Platform Experience Required Reports To: VP, Product Engineering Function: Product Engineering — works across multiple platforms Work Mode: Work From Office You will join the product engineering group and work across several AI-native platforms, not a single product . You report to the VP of Product Engineering, who owns platform-wide architecture and the roadmap. Inside the agent platform, the design and the delivery are yours: you turn direction into specifications, lead a small pod through them, write a good share of the code yourself, and own what ships.

Our stack: Next.js and React Native on the front end

- Java and Spring Boot services
- Python, FastAPI and LangGraph in the AI layer
- MongoDB Atlas with vector search, Redis, Kafka and Elasticsearch
- AWS and Oracle Cloud. This is a hands-on lead role, not a management one. We build with AI coding agents every day — Claude Code most of all. You structure repositories so an agent inherits the architecture rather than inventing one: context files, conventions, custom commands, sub-agents, hooks, MCP servers wired into your own loop. We would genuinely like to see what you have built — repositories, context files, write-ups, or a walkthrough of a project you are proud of. Human-in-the-loop and approval gates that genuinely halt execution, with a test proving it. Build-versus-buy across LLM providers, vector stores, orchestration frameworks and voice vendors, weighed on capability, cost, latency and lock-in. Choosing the right technique for each problem — retrieval, a tool call, a fine-tune, or a deterministic rule. Retrieval and vector strategy on MongoDB Atlas — chunking, hybrid search, reranking, freshness. Evaluation and quality Evaluation suites and behavioural regression testing that run in CI, not in a notebook. Golden datasets drawn from real user interactions, grown from production failures.





Quality gates for agent changes: what must pass before a graph change can merge. Responsible AI and guardrails Personal data in prompts, logs and traces — handling, retention and GDPR-aligned practice. Run the pod's cadence — scope, daily unblocking, dependencies with the other pods, and honest escalation early when a date is at risk.

Own the AI-native engineering practice for your pod — the context files, the templates, the conventions, the review habits — and help raise it across the group. Our products sit in travel technology — booking, distribution and servicing across several channels. You do not need to arrive as a travel expert, but you will need to become one, because most serious agent failures in this space are domain failures wearing an engineering costume.

Content arrives from GDS, NDC and direct connections with different latency and reliability. Travel domain depth is a real advantage and we weight it. 8–9 years of engineering experience, with a solid backend or distributed-systems foundation established before the LLM work. ~2+ years building and shipping LLM-based agentic systems in production — real users, real incidents, real fixes. ~ Hands-on LangGraph or an equivalent orchestration framework, and Amazon Bedrock or comparable LLM infrastructure

- Strong Python , including async patterns and testing strategies for non-deterministic components. ~ Experience leading a small team or workstream to dates that mattered , and taking a prototype through to production. ~ Transparent written and spoken English — you will be specifying behaviour for systems that behave probabilistically. Travel technology — booking, distribution, GDS or NDC — or another domain where a wrong answer has financial or regulatory consequence. AWS depth beyond Bedrock; exposure to Oracle Cloud. Responsible-AI or model governance practice. Retrieval at scale — hybrid search, reranking, freshness under constant ingest. Your LLM work has been prompt design and integration rather than building systems. You are looking to step back from coding and lead through others — this pod is small and you are in it.

📌 Senior Engineer, Product Design Engineering (Hyderabad)
🏢 Mondee
📍 Hyderabad