LLM Systems / AI Agent Engineer (Remote, Full-Time) [AS311]

Team: Engineering

Location: India, Hyderabad, Bangalore, Mumbai, Pune, Delhi NCR, Mohali

Commitment: Full-Time | Remote

Workplace Type: remote

Responsibilities

Build and evolve production AI agents on foundation models, currently AWS Bedrock.
Develop and evolve orchestration for production AI agents.
Apply context engineering to production LLM and agent systems.
Build and maintain evaluation datasets and pipelines, including tool-selection, trajectory and LLM-as-judge evaluations.
Build and maintain production observability and monitoring for LLM and agent systems.
Implement and work with tracing and instrumentation for production LLM systems.
Work directly alongside the engineer currently leading the AI-agenting function as the first dedicated hire in this area.
Apply strong agent-architecture fundamentals to help inform whether the existing custom orchestration layer should be retained or a production framework adopted.
Work as part of a new three-person product team alongside the AI function lead and a Full Stack Engineer.
Operate as an individual contributor working alongside the AI function lead rather than managing others.
Take ownership of measurable deliverables immediately upon onboarding.
Deliver similar AI/agent engineering work against roadmap timelines.

Requirements

2+ years of experience building production LLM agents, including tool-calling agent loops, streaming, context management, structured outputs and orchestration frameworks.
Production experience with an agentic framework such as LangGraph, LangChain or custom orchestration. There is no fixed orchestration framework requirement; strong agent-architecture fundamentals and production experience with any agentic framework are in scope. - 1.5+ years of experience with evaluation-driven development, including building and maintaining evaluation datasets and pipelines covering tool selection, trajectory evaluation and LLM-as-judge.
1+ year of experience with LLM observability, tracing and instrumentation using Langfuse, OpenTelemetry or similar tooling.
Genuine production agent-observability exposure. Direct, hands-on Langfuse experience is strongly preferred because this is a confirmed skill gap within the team; OpenTelemetry or other tracing tools are acceptable only as a secondary signal alongside real agent-observability exposure. - 1+ year of experience with LLM cost optimisation, including prompt caching, model selection and routing, and LLM FinOps.
5+ years of backend engineering proficiency, including TypeScript/Node, Postgres and serverless AWS.
Proven experience delivering similar work on similar timelines.
Experience shipping agentic AI systems to production, with the ability to speak to concrete failure modes and mitigations and operate end-to-end across the AI stack.

Nice to Have

1+ year of AWS Bedrock experience. Equivalent production experience with other foundation-model providers, including OpenAI, Anthropic API, Azure OpenAI or Vertex AI, is fully transferable.
1+ year of experience with AI safety and guardrails, including prompt-injection screening, output validation and handling untrusted input.
6+ months of familiarity with MCP and multi-agent patterns.
Familiarity with geospatial data.

Benefits

Fixed Shifts: 12:00 PM - 9:30 PM IST (Summer) | 1:00 PM - 10:30 PM IST (Winter)
No Weekend Work: Real work-life balance, not just words
Day 1 Benefits: Laptop and full medical insurance provided
Support That Matters:Mentorship, community, and forums where ideas are shared
True Belonging: A long-term career where your contributions are valued