AI Engineer

Overview

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). Rather than managing teams or owning delivery outcomes, these practitioners work directly within teams to experiment, apply AI in real workflows, and demonstrate what is possible. Their impact comes from doing the work alongside teams , surfacing what works, and codifying repeatable patterns that others can adopt and scale.

The role is designed for innovators who thrive in ambiguity, lead through influence, and accelerate adoption by example. Success is measured by the clarity and reusability of the patterns they leave behind, not by headcount, reporting lines, or program ownership.

Role

Knowledge

• 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.

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.

About you

Experience

• 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.

Outcomes

• Tangible examples of AI-driven improvements you personally helped teams achieve, such as reduced cycle time, improved product quality, increased test coverage, or reduced operational toil.
• Clear evidence of measurable delivery impact from prior AI initiatives, such as reduced development cycle time, increased test automation and coverage, improved production stability, or reduced operational toil.
• Proven ability to translate experimentation into sustainable capability by leaving behind reusable patterns, standards, and self-sustaining practices.
• Track record of helping organizations adopt AI responsibly, balancing speed with risk mitigation and embedding controls rather than treating governance as a separate gate.
• Proven ability to leave behind self-sustaining capabilities , not dependency, by turning experimentation into repeatable, well-documented patterns.