Transformation Engineer

Role overview Lead hands-on AI transformation within a client organization of roughly 500 people, building the technical foundations and practices that help teams use AI in everyday work. The role combines software engineering, production agent development, knowledge management, and organization-wide teaching, with responsibility for taking solutions from early discovery through adoption and ongoing improvement.

Responsibilities - Build and maintain an LLM-powered knowledge system that makes institutional information useful to employees and agents. - Design memory, context, retrieval, indexing, and refresh strategies, including permission-aware access and information quality controls. - Deliver production agent applications and supporting harnesses, with evaluations, monitoring, safeguards, testing, human oversight, and iteration. - Establish practical conventions and workflows for Claude Code, including instructions, skills, subagents, hooks, and integrations. - Build reusable developer tools, APIs, integrations, and automation; deploy and operate solutions on AWS with security, reliability, and cost controls. - Create and deliver training, workshops, onboarding, and documentation for technical and non-technical audiences; track adoption and improve enablement. - Partner across business, engineering, IT, and security to select use cases, manage AI governance, and carry solutions into production and sustained use.

Requirements - Strong software or AI engineering experience, with production delivery skills in Python, TypeScript, JavaScript, or a comparable language. - Demonstrated work building LLM knowledge or retrieval systems such as RAG, enterprise search, or context-engineering solutions. - Practical expertise in agent memory, retrieval, context management, and permission-aware information access. - Deep, hands-on Claude Code experience, with examples of workflows, skills, subagents, integrations, or harnesses you have built. - Proven ownership of production agent systems, including evaluation, observability, safeguards, debugging, maintenance, and improvement. - AWS experience, including services such as Lambda, Bedrock, IAM, S3, or vector search, with attention to security, scale, and cost. - Experience guiding technology or AI adoption, teaching teams, and explaining technical ideas to non-engineers.

Nice to have - Experience measuring adoption and outcomes across a large organization and establishing practical governance for privacy, security, and responsible AI use.