This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a LLM Systems / AI Agent Engineer based in India.

This is an opportunity to build and evolve production-grade AI agents powered by modern foundation models.

You will work across agent orchestration, context engineering, evaluation, observability, and production monitoring.

The role is focused on practical LLM systems engineering rather than traditional machine-learning model training.

As the first dedicated hire in this area, you will have meaningful influence over the architecture and technical direction of the agent platform.

You’ll work closely with an experienced AI engineering lead and a small, highly focused product team.

The environment is hands-on and delivery-oriented, with measurable responsibilities from the start.

This is a strong fit for an engineer who enjoys owning complex AI systems end to end and turning emerging agent patterns into reliable production technology.

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Accountabilities:

- Build, deploy, and continuously improve production AI agents using foundation models, including AWS Bedrock and comparable providers.
- Design and evolve orchestration systems supporting tool calling, streaming, context management, structured outputs, and agent workflows.
- Apply context-engineering techniques to improve the reliability, quality, and effectiveness of LLM and agent systems.
- Develop and maintain evaluation datasets and pipelines covering tool selection, agent trajectories, and LLM-as-judge evaluations.
- Establish production observability, monitoring, tracing, and instrumentation for LLM and agent workloads.

- Contribute architectural expertise to evaluate the existing custom orchestration layer and determine whether a production framework such as LangGraph or LangChain would provide additional value.
- Work closely with the AI function lead and Full Stack Engineer as part of a focused three-person product team.
- Operate as an individual contributor, taking ownership of clearly defined and measurable deliverables from onboarding onward.





- Identify production failure modes, implement appropriate mitigations, and contribute across the AI engineering stack.
- Optimize LLM operating costs through prompt caching, model selection, routing strategies, and broader LLM FinOps practices.
- Help establish robust engineering practices for reliable, scalable, and maintainable agentic AI systems.

Requirements:

- 2+ years of professional experience building production LLM agents, including tool-calling loops, streaming, context management, structured outputs, and orchestration.
- Production experience with an agentic framework such as LangGraph, LangChain, or a custom orchestration solution, combined with strong understanding of agent architecture patterns.
- 1.5+ years of experience with evaluation-driven development, including evaluation datasets and pipelines for tool selection, trajectory assessment, and LLM-as-judge approaches.
- 1+ year of hands-on experience with LLM observability, tracing, and instrumentation using tools such as Langfuse, OpenTelemetry, or equivalent platforms; direct production experience with agent observability is particularly valuable.

- 1+ year of experience optimizing LLM costs through prompt caching, model selection and routing, and LLM FinOps.
- 5+ years of backend engineering experience, including strong proficiency with TypeScript/Node.js, PostgreSQL, and serverless AWS technologies.
- Proven track record of shipping agentic AI systems to production and delivering comparable engineering projects against defined timelines.

- Ability to discuss real-world agent failure modes, reliability challenges, and mitigation strategies with strong technical depth.
- Ability to operate independently across the AI stack while collaborating effectively within a small,



specialized engineering team.
- Experience with AWS Bedrock is an advantage, while production experience with other foundation-model providers such as OpenAI, Anthropic, Azure OpenAI, or Vertex AI is transferable.

- Knowledge of AI safety and guardrails, including prompt-injection protection, output validation, and handling untrusted inputs, is a plus.
- Familiarity with MCP and multi-agent architectures is beneficial.
- Exposure to geospatial data is an additional advantage.

Benefits:

- Fully remote, full-time working arrangement in India.
- Fixed working hours: 12:00 PM–9:30 PM IST during summer and 1:00 PM–10:30 PM IST during winter.
- No weekend work, supporting a strong work-life balance.
- Laptop provided from day one.
- Full medical insurance from the start of employment.
- Access to mentorship, professional communities, and knowledge-sharing forums.

- Supportive, collaborative environment focused on continuous learning and professional development.
- Opportunity to take meaningful ownership within a small, specialized AI product team.
- Long-term career chance where individual contributions and technical impact are valued.

nHow Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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📌 LLM Systems / AI Agent Engineer (India)
🏢 Jobgether
📍 India