AI Engineering Manager

This role is an experienced manager, who is responsible for building and leading a team to deliver technology products and services that meet business outcomes. Their primary responsibility is the people they manage, ensuring they are developing through coaching, mentoring, feedback, and hands-on career development. Other key responsibilities may include developing a technology strategy and ensuring technology solutions comply with applicable standards.

This role advocates and advances modern, Agile solution delivery practices and evangelizes great design, engineering and organizational practices.

• Lead refinement of stories and technical requirements for AI agent use-cases, orchestration workflows, and deterministic automation capabilities.
• Participate in and help guide estimation of work necessary to deliver stories and requirements across both agentic and deterministic automation implementations
• Perform spikes, proofs of concept, and technical experiments to reduce delivery risk, validate design choices, and inform implementation direction
• Design, code, and review solution components and unit tests to deliver requirements and stories in accordance with acceptance criteria, control requirements, and engineering standards.
• Build and enhance reusable components that support AI-enabled workflows, agent interactions, orchestration logic, deterministic automation routines, and integration with enterprise tools and services.
• Utilize multiple architectural components across data, application, workflow, and automation layers in the design and development of complex requirements.
• Help resolve complex technical issues involved in realizing story work, including integration, orchestration, execution logic, runtime reliability, and supportability concerns.
• Contribute to and improve test suites, analyze test results, identify defects or failure points, and help triage and remediate underlying causes.
• Document and communicate required information for deployment, maintenance, support, and business functionality of developed solutions, ensuring operational teams can support them effectively.
• Participate in and help guide delivery and release events, including branching, pull requests, issue triage, merge and conflict resolution, release notes, and implementation support.
• Support secure, observable, and supportable solution delivery by implementing and promoting appropriate logging, validation, exception handling, resiliency measures, and operational controls.
• Mentor fewer senior engineers through code reviews, technical guidance, and day-to-day implementation support to improve quality, consistency, and team capability.
• Contribute to reusable engineering patterns, implementation standards, and delivery practices that improve scalability, consistency, and speed across both pillars.
• Work flexibly across Engineering Delivery and Deterministic Automation Orchestration priorities, supporting whichever implementation demand is most critical while maintaining delivery quality and technical discipline.
• Strong experience developing complex software or automation solutions that translate business requirements into scalable, maintainable, and supportable technical capabilities.
• Advanced programming and scripting skills in languages commonly used for software engineering, API development, orchestration, and automation implementation.
• Demonstrated ability to work across both AI-enabled use-cases and deterministic automation patterns, with flexibility to support either based on delivery demand.
• Experience designing and building reusable components, services, and integration patterns that connect applications, tools, data, and workflows.
• Strong understanding of workflow orchestration, execution control, task sequencing, and operational logic required to move from intent or decisioning to controlled action.
• Experience contributing to AI agent or workflow-based solutions, including agent logic, tool interaction patterns, supporting services, and execution flow design.
• Strong knowledge of software development lifecycle practices including design, coding, code review, testing, version control, release management, and…