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

August 20, 2026

About Smart Working At Smart Working, we believe your job should not only look right on paper but also feel right every day. We're one of the highest-rated workplaces on Glassdoor, connecting exceptional professionals with outstanding global teams and products through long-term remote opportunities. Our mission is to break down geographical barriers and create meaningful career opportunities where talented individuals can thrive, grow, and make a genuine impact. When you join Smart Working, you become part of a supportive and collaborative community that values integrity, excellence, continuous learning, and professional growth. We provide the tools, support, and environment needed to help you succeed while enjoying the flexibility of a truly remote-first workplace. About the Role As an LLM Systems / AI Agent Engineer, you will focus on building and evolving production AI agents on foundation models, covering orchestration, context engineering, evaluation pipelines, and production observability and monitoring. This is an LLM systems and AI agent engineering position rather than a traditional ML model-training role. You will be the first dedicated AI-agenting hire, working directly alongside the engineer who currently leads this function. The product currently uses a custom orchestration layer, and your knowledge of agent architecture patterns will help inform whether to continue with this approach or adopt a production framework such as LangGraph or LangChain. Roadmap timings are somewhat tentative, but you will be assigned measurable deliverables immediately upon onboarding. Responsibilities <ul> <li>Build and evolve production AI agents on foundation models, currently AWS Bedrock.</li> <li>Develop and evolve orchestration for production AI agents.</li> <li>Apply context engineering to production LLM and agent systems.</li> <li>Build and maintain evaluation datasets and pipelines, including tool-selection, trajectory and LLM-as-judge evaluations.</li> <li>Build and maintain production observability and monitoring for LLM and agent systems.</li> <li>Implement and work with tracing and instrumentation for production LLM systems.</li> <li>Work directly alongside the engineer currently leading the AI-agenting function as the first dedicated hire in this area.</li> <li>Apply strong agent-architecture fundamentals to help inform whether the existing custom orchestration layer should be retained or a production framework adopted.</li> <li>Work as part of a new three-person product team alongside the AI function lead and a Full Stack Engineer.</li> <li>Operate as an individual contributor working alongside the AI function lead rather than managing others.</li> <li>Take ownership of measurable deliverables immediately upon onboarding.</li> <li>Deliver similar AI/agent engineering work against roadmap timelines.</li> </ul> Requirements <ul> <li>2+ years of experience building production LLM agents, including tool-calling agent loops, streaming, context management, structured outputs and orchestration frameworks.</li> <li>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.</li> <li>1+ year of experience with LLM observability, tracing and instrumentation using Langfuse, OpenTelemetry or similar tooling.</li> <li>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.</li> <li>5+ years of backend engineering proficiency, including TypeScript/Node, Postgres and serverless AWS.</li> <li>Proven experience delivering similar work on similar timelines.</li> <li>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.</li> </ul> Nice to Have <ul> <li>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.</li> <li>1+ year of experience with AI safety and guardrails, including prompt-injection screening, output validation and handling untrusted input.</li> <li>6+ months of familiarity with MCP and multi-agent patterns.</li> <li>Familiarity with geospatial data.</li> </ul> Benefits <ul> <li>Fixed Shifts: 12:00 PM - 9:30 PM IST (Summer) | 1:00 PM - 10:30 PM IST (Winter)</li> <li>No Weekend Work: Real work-life balance, not just words</li> <li>Day 1 Benefits: Laptop and full medical insurance provided</li> <li>Support That Matters:Mentorship, community, and forums where ideas are shared</li> <li>True Belonging: A long-term career where your contributions are valued</li> </ul> We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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