Lead, AI Engineering & SDLC Automation
United StatesFull-TimeManager
SalaryUSD 168160 - 315300 / year
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Job Details
- Experience
- 5+ years
- Required Skills
- CI/CDDevOps
Requirements
- 5+ years of experience formally or informally leading people, projects, and/or programs.
- Bachelor’s or Master’s degree or equivalent experience.
- Strong background in AI-enabled engineering, platform engineering, DevOps, developer productivity, workflow automation, or SDLC automation.
- Experience leading teams, programs, or cross-functional initiatives in complex software engineering environments.
- Experience translating organizational strategy into roadmaps, execution plans, governance models, and measurable outcomes.
- Experience with AI agents, copilots, AI-assisted software engineering tools, ML-driven automation, or agentic workflow automation.
- Strong understanding of CI/CD, SDLC tooling, developer workflows, engineering productivity tooling, and secure integration patterns.
- Understanding of responsible AI adoption, AI governance, usage controls, safety guardrails, and secure deployment patterns.
- Ability to influence senior stakeholders across engineering, product, security, QE, IT, and platform organizations.
- Experience driving adoption, enablement, and behavior change across distributed engineering teams.
Responsibilities
- Lead and develop the AI Engineering & Automation team responsible for AI-assisted SDLC automation, developer workflow integration, and engineering productivity tooling.
- Define team priorities, resource needs, operating cadence, performance expectations, and execution plans.
- Own the roadmap for AI-enabled engineering automation across PSD, from tool selection and integration to adoption, measurement, and continuous improvement.
- Deliver AI-assisted engineering tools, agentic workflows, copilots, and workflow automation with measurable productivity uplift.
- Integrate AI automation into CI/CD pipelines, developer paved roads, engineering workflows, and platform services.
- Establish governance, safety guardrails, evaluation criteria, usage standards, and secure integration patterns for responsible AI adoption in engineering workflows.
- Partner with DevSecOps, Developer Experience, Security, QE, IT, and engineering teams to embed automation into software delivery practices.
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