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