AI Engineering Manager

Key Responsibilities

• 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.

Requirements

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 deployment support.

Experience building and reviewing unit, integration, regression, or automated testing approaches to improve quality and production readiness.

Able to interpret and refine functional and non-functional requirements and convert them into robust technical solutions with clear implementation paths.

Experience with CI/CD practices and the ability to support build, test, release, and deployment workflows in a controlled enterprise environment.

Strong understanding of logging, monitoring, resiliency, error handling, supportability, and operational controls required for production-grade engineering solutions.

Ability to work across architecture, engineering, automation, and delivery teams to resolve complex technical issues and support integrated outcomes.

Experience mentoring less senior engineers or contributing to technical guidance, code quality, and implementation standards across a team.

Strong written and verbal communication skills, including the ability to document technical approaches, integration needs, design considerations, and operational support requirements.

Experience working in a fast-paced and complex environment with evolving priorities, multiple dependencies, and iterative delivery expectations.

Strong analytical thinking, problem-solving ability, and technical judgment in support of high-quality engineering execution and delivery decisions.