AI Builder

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.

• 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.

• 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, Event Bridge, 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.

• 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.