Lead, AI Engineering – SDLC Automation

Role Overview

• 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.
• Prioritize AI automation investments based on productivity uplift, developer experience, quality, reliability, security, cost, and platform strategy.
• Drive cross-organization adoption through enablement, documentation, training, feedback loops, and measurable adoption plans.
• Resolve conflicting priorities across engineering productivity, security, reliability, cost, and responsible AI adoption.
• Define and track success measures for AI-enabled engineering automation, including productivity, adoption, toil reduction, quality, reliability, and developer experience.

Requirements

• Typically 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.
• Ability to balance speed, productivity, quality, reliability, security, cost, and responsible AI use.
• Strong analytical and communication skills, with the ability to translate productivity and adoption insights into practical roadmap decisions.

Tech Stack

• SDLC

Benefits

• health insurance
• retirement plans
• paid time off
• flexible work arrangements
• professional development
• bonuses
• stock options