AI Transformation Engineer

About the Job

As an AI Transformation Engineer, you will lead the journey from ad-hoc AI usage to AI-native software delivery across a 300-person organization. You will design and execute the transformation roadmap, establish responsible AI guardrails, and embed AI-powered practices throughout the SDLC (from coding and testing to operations and support).

You will enable teams with training, playbooks, and tooling, scale proven use cases, and define metrics to measure adoption, productivity, quality, and risk reduction. You will pre-pare and present your approach and plan to the leadership team, ensuring clear outcomes, governance, and a pragmatic path to value.

What you do

• Define responsible AI standards, guardrails, and governance (security, privacy, compliance, IP) with Architecture, Security, and Legal.
• Stand up an enablement program (training, playbooks, communities of practice) and a use case factory to pilot, prove, and scale value.
• Embed AI across the SDLC: AI coding assistants, test generation, code review, documentation, incident triage, analytics, and AIOps.
• Provision and standardize tooling (e.g., Copilot/Claude, prompt guidelines, evaluation harnesses, CI/CD integrations) and support onboarding.
• Build accelerators (templates, pipelines, prompt libraries, MLOps foundations) to industrialize repeatable AI patterns. Define and track KPIs/OKRs (velocity, quality, MTTR, risk, cost); instrument dashboards and run regular business reviews.
• Drive change management, stakeholder communications, and training; coach champions and team leads to sustain adoption.
• Prepare and deliver leadership presentations on strategy, plan, risks, and results; run steering cadence and course correct quickly.

What you bring

• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent work experience).
• 8+ years in software delivery, including 3+ years leading AI enablement or transformation at scale (100+ engineers).
• Hands on experience with AI coding tools (e.g., GitHub Copilot, Claude Code) and applying LLMs/NLP to SDLC work-flows.
• Solid foundations in DevSecOps, CI/CD, testing automation, and observability; familiarity with MLOps and evaluation practices.
• Proven change leadership: training programs, playbooks, champions networks, and measurable adoption/ROI.
• Strong stakeholder management and executive communication; able to present clear plans, risks, and outcomes.
• Experience defining guardrails for responsible AI (security, privacy, IP, compliance) and aligning with enterprise standards.