SENIOR AI AUTOMATION ENGINEER
POSITION SUMMARY
The Senior AI Automation Engineer designs, builds, and maintains global automations and AI agents that support the Engineering Centers of Excellence (JET, Quality, and DevOps) and the cross-LOB programs run by the Engineering PMO. This role turns manual, repetitive coordination, reporting, and quality workflows into reliable, observable, and reusable automation that works across geographies and business lines.
The role also owns the governance of the AI skills and agents the organization creates: how they are designed, reviewed, versioned, stored, secured, and retired. The ideal candidate combines strong software engineering fundamentals with hands-on experience building LLM-based agents and workflows in production, and treats automation as a product with users, standards, and measurable outcomes. This role reports to the Tech Lead, Project Management and works closely with CoE leads, Agile Leaders, and LOB engineering teams.
KEY RESPONSIBILITIES
Global Automation Design and Delivery
• Design, build, and operate automations that remove manual work from program management, reporting, quality gates, release, and support workflows across all LOBs.
• Integrate with the systems teams already use (Jira, Confluence, GitHub, CI/CD, observability, and collaboration tools) so automation is embedded in existing workflows rather than adding new ones.
• Translate stakeholder needs from the CoEs and the PMO into prioritized automation backlogs with clear scope, owners, and success measures.
• Build with reliability in mind: error handling, retries, logging, alerting, and runbooks so automations can be supported by others.
AI Agents for the Centers of Excellence
• Create and maintain AI agents and workflows that support the JET, Quality, and DevOps CoEs, such as status and health reporting, requirement and epic validation, adoption dashboards, and triage support.
• Partner with CoE leads to define agent behavior, guardrails, and escalation paths, ensuring outputs are accurate, traceable to source data, and appropriate for leadership consumption.
• Evaluate agent quality with test sets, regression checks, and feedback loops, and improve prompts, tools, and context over time.
• Monitor usage, accuracy, cost, and adoption of each agent, and report on the value delivered.
Skills and Repository Governance
• Own the governance framework for AI skills and agents: naming conventions, structure, documentation standards, ownership, review and approval, versioning, and deprecation.
• Maintain the central repositories for skills, agents, and shared prompts, including access controls, branching and release practices, and change history.
• Define and enforce security and data-handling requirements, including secrets management, least-privilege access, and handling of sensitive or customer data, in partnership with Security.
• Run intake and review for new skills created by other teams, provide templates and guidance, and prevent duplication by promoting reuse.
• Maintain an inventory of active skills and agents with owners, dependencies, and health status, and retire those that are unused or outdated.
Program and PMO Partnership
• Work with the Tech Lead and Project Managers to automate program-level coordination, including status consolidation, dependency and risk tracking, and leadership reporting.
• Operate as a neutral, shared resource that serves the collective priorities of the CoEs and the PMO rather than any single group.
• Communicate progress, risks, and trade-offs clearly to technical and non-technical stakeholders across geographies.
AI Enablement and Standards
• Coach CoE and PMO team members on building and using skills, agents, and automation safely and effectively.
• Publish reusable components, examples, and best practices to build collective capability and reduce duplicated effort.
• Stay current on AI tooling available within Sovos and recommend adoption where it aligns with organizational standards and needs.
QUALIFICATIONS
Required
• 5+ years of professional software engineering, automation, or DevOps experience, with a track record of shipping and supporting production systems.
• Hands-on experience building LLM-based agents, skills, or workflows (for example with Claude, tool use, prompt design, and context management) beyond personal productivity use.
• Strong programming skills in Python and/or JavaScript/TypeScript, with experience building integrations using REST APIs, webhooks, and automation platforms.
• Experience with Git-based workflows, code review, CI/CD, and managing shared repositories, including versioning, access control, and release practices.
• Working knowledge of secure development practices, including secrets management, permissions, and handling of sensitive data.
• Ability to evaluate AI output quality, design tests for agents, and iterate based on measured results.
• Strong communication skills, with the ability to gather requirements from non-technical stakeholders and explain technical decisions clearly.
• Comfort working autonomously in a distributed, multi-geography environment, with professional English proficiency.
Preferred
• Experience with Jira and Confluence automation and APIs, and with MCP or similar tool-integration approaches.
• Experience defining standards or governance for shared engineering assets, such as internal libraries, templates, or platform tooling.
• Familiarity with observability, metrics, and dashboarding (for example DORA metrics or quality gates).
• Experience supporting a Center of Excellence, PMO, platform, or enablement team.
• Understanding of SDLC, Agile/Scrum, QA practices, and release management.
• Experience in regulated or compliance-focused software environments.
Note: Nothing in this job description restricts management's right to assign or reassign duties and responsibilities to this role at any time.
