Design and implement production-grade agentic systems; autonomous agents that leverage LLMs to reason, use tools, manage context, and orchestrate complex multi-step workflows on behalf of users.
Build scalable backend services, APIs, and data pipelines that support real-time AI inference, conversation state, and tool execution with robust error handling and recovery.
Architect systems for scale, designing AI infrastructure that handles growing inference demands, evolving agent complexity, and production reliability requirements.
Build and maintain RAG pipelines, including embeddings, vector search, and retrieval strategies, to power knowledge-driven features and contextual AI responses.
Bring foundational ML understanding to inform decisions around model selection, fine-tuning, embeddings, and evaluation and knowing when to use off-the-shelf models versus more custom approaches.
Collaborate with product, engineering, DevOps, and QA teams to define requirements and deliver AI-powered features, leading technical direction while pulling in the right people at the right stages.
Evaluate emerging agentic frameworks, LLM providers, and AI tooling; recommend adoptions that move the team’s capabilities forward.
Establish and document AI engineering standards, covering reliability, safety, observability, and prompt management and building institutional knowledge as the AI function grows.
Maintain engineering excellence. Write clean, well-tested code; conduct thorough code reviews; champion best practices across the team
Requirements
Bachelor’s degree in Computer Science, Engineering, or a related technical field
6+ years of professional software engineering experience
2+ years of hands-on experience designing and building applications that integrate agentic systems in production environments
Experience with cloud platforms, preferably AWS (Lambda, Bedrock, OpenSearch)
Proven track record of shipping and operating production systems with reliability and observability
Strong debugging, troubleshooting, and problem-solving skills
Ability to work independently with a high degree of ownership, driving projects forward with limited direct oversight
Strong communication skills with the ability to collaborate effectively across engineering, product, and non-technical stakeholders.