AI Transformation Architect (PDLC)

About the role

This role is a hands-on, embedded individual contributor that acts as a guide and coach to product and engineering teams as they adopt AI across the Product Development Lifecycle (PDLC).

Responsibilities

• Work directly within teams to experiment, apply AI in real workflows, and demonstrate what is possible.
• Surfacing what works and codifying repeatable patterns that others can adopt and scale.
• Measure AI impact on engineering and product outcomes, such as cycle time, defect rates, test coverage, and operational stability.
• Embed directly with teams and coach through hands-on application, working shoulder-to-shoulder with engineers, product managers, and QA to apply AI in real scenarios.
• Define and operationalize AI standards, patterns, and best practices, including usage guidelines, prompt conventions, reference architectures, and reusable templates.
• Design and deliver enablement programs, including playbooks, training materials, workshops, and hands-on coaching that translate AI concepts into practical day-to-day application.
• Integrate AI solutions into existing toolchains, such as IDEs, CI/CD pipelines, testing frameworks, and monitoring platforms, including rapid prototyping to demonstrate value.
• Influence executives and earn credibility with engineering and product teams, addressing concerns and driving adoption through visible outcomes.
• Define a phased roadmap for AI adoption across the PDLC that aligns with business objectives and ties each initiative to clear value.
• Bridge product, engineering, devops, security, and compliance, ensuring AI improvements are coordinated rather than siloed.
• Redesign workflows to fully leverage AI capabilities, not simply automate existing steps.
• Drive toward measurable results while maintaining quality, safety, and compliance, and continuously refine approaches based on feedback and data.

Requirements

• Deep understanding of the full PDLC, from ideation and requirements through design, development, testing, security, deployment, and operations, including where AI can meaningfully augment each stage.
• Strong working knowledge of modern AI tooling, particularly generative AI assistants, automation frameworks, developer assistances (e.g. GHCP, Claude Code), and current / emerging best practices, with the ability to evaluate and adopt tools pragmatically rather than by vendor alignment.
• Solid grounding in responsible AI, including data privacy, security, model risk management, and ethical principles, with experience embedding governance and compliance controls directly into delivery workflows.
• Familiarity with defining and tracking metrics to measure AI impact on engineering and product outcomes, such as cycle time, defect rates, test coverage, and operational stability.

Qualifications

• Ten or more years of experience in software development, platform engineering, or technology consulting, with significant exposure to AI-enabled or DevOps-driven transformation initiatives.
• Demonstrated success working in large, complex, and regulated enterprise environments, with hands-on experience navigating governance, security, and compliance constraints.
• Prior experience acting as a change agent, program lead, consultant, or internal champion, influencing teams without formal authority and engaging both senior leaders and delivery teams.
• History of building repeatable assets such as playbooks, toolkits, templates, or reference models that scale beyond individual teams and reduce dependency on ongoing staff augmentation.

Skills

• Ability to embed directly with teams and coach through hands-on application, working shoulder-to-shoulder with engineers, product managers, and QA to apply AI in real scenarios.
• Ability to define and operationalize AI standards, patterns, and best practices, including usage guidelines, prompt conventions, reference architectures, and reusable templates.
• Proven capability to design and deliver enablement programs, including playbooks, training materials, workshops, and hands-on coaching that translate AI concepts into practical day-to-day application.
• Technical proficiency in integrating AI solutions into existing toolchains, such as IDEs, CI/CD pipelines, testing frameworks, and monitoring platforms, including rapid prototyping to demonstrate value.
• Strong communication and change leadership skills, with the ability to influence executives and earn credibility with engineering and product teams, addressing concerns and driving adoption through visible outcomes.

Abilities

• Strategic thinker who can define a phased roadmap for AI adoption across the PDLC that aligns with business objectives and ties each initiative to clear value.
• Strong cross-functional influencer capable of bridging product, engineering, devops, security, and compliance, ensuring AI improvements are coordinated rather than siloed.
• Systems-oriented problem solver who can redesign workflows to fully leverage AI capabilities, not simply automate existing steps.
• Outcome-focused and adaptable leader who drives toward measurable results while maintaining quality, safety, and compliance, and continuously refines approaches based on feedback and data.