AI Delivery Engineer - AWS Bedrock

Role overview

Build and deliver generative AI applications with client teams, taking work from discovery through prototyping, production release, and handoff. The position combines hands-on engineering—primarily with Amazon Bedrock and related AWS services—with direct work alongside business users and technical teams. Travel may reach 70%, typically Monday through Thursday, for on-site client work.

Responsibilities - Map client workflows and turn needs into use cases, delivery plans, and measurable acceptance criteria. - Prototype early and test against representative data and user feedback. - Build LLM applications and agent workflows with retrieval, grounding, tool calling, and APIs using Python and AWS AI services. - Integrate solutions with client systems and approved data sources, applying authentication and permission controls. - Develop automated tests, AI evaluations, deployment pipelines, monitoring, and error handling; assess quality, safety, latency, reliability, and cost. - Configure guardrails and coordinate with security, privacy, and risk teams on technical controls, audit evidence, and human review. - Co-build with client engineers, provide operating documentation and knowledge transfer, and contribute reusable engineering assets.

Requirements - 4 or more years building and deploying production software, including at least 3 years writing Python in production. - At least 3 years building on AWS, including Lambda and IAM. - At least 1 year building LLM applications, such as systems using retrieval, structured outputs, tool calling, or agent workflows. - Experience working with clients or business stakeholders to clarify requirements and demonstrate progress. - Experience with version control, automated testing, and deployment pipelines. - Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience. - Ability to travel up to 70% for client work.

Nice to have - Hands-on experience with Bedrock, its agent, knowledge base, or guardrail services, or SageMaker. - A production application using a foundation model through Bedrock or another model API; experience with AI evaluations, adversarial testing, or observability. - Enterprise search, document processing, data pipelines, API or Model Context Protocol integrations, or proficiency in TypeScript, Java, or Go. - AWS certifications, financial services or other enterprise-sector experience, or experience mentoring engineers.