Remote job
Staff AI Builder
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About this role
Role overview Lead the architecture and delivery of production AI and machine-learning systems, taking ownership from model and workflow design through integration, deployment, and ongoing reliability. You’ll guide technical decisions on a cross-functional team, solve complex integration challenges, and help establish engineering practices for secure, scalable AI products.
Responsibilities - Design agent workflows, including tool calling and single- or multi-agent orchestration, choosing patterns to fit the problem. - Build retrieval-augmented generation pipelines with chunking, embeddings, vector search, reranking, and refresh handling. - Develop prompts, integrations, evaluation methods, and observability for production AI systems. - Build and maintain services and APIs using Python, Node.js, and cloud infrastructure; support event-driven and serverless workflows. - Design data models for conversation state and agent memory, and add resilience through retries, circuit breakers, and fallback models. - Lead architecture discussions, mentor engineers, and own features through release, debugging, and production hardening.
Requirements - At least six years of professional software engineering experience, including production GenAI or LLM systems. - Practical experience with agentic workflows, RAG, prompt design, and evaluating and monitoring LLM applications. - Strong Python and Node.js skills, including experience building full-stack applications and REST APIs. - Hands-on experience with AWS AI and application services, such as Bedrock, Lambda, DynamoDB, S3, SQS, EventBridge, or Step Functions. - Knowledge of production reliability patterns and containerized, cloud-native deployment. - Ability to take ownership of ambiguous integration work and explain technical tradeoffs in depth.
Nice to have - Experience with Bedrock Agent Core, MCP tool integration, or state-machine workflow orchestration. - Background in regulated or high-stakes domains, or experience running LLM observability tools in production.