AI Adoption Coach – Product Development Lifecycle (PDLC)

CEdge Inc•O’Fallon, MO

4d•Onsite

About The Position

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.

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.
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.
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.
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.
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.
Expertise in high volume, low latency transaction processing systems (such as payments transaction switching, high frequency trading systems or global real-time event streaming systems etc.).
Experience with various coding languages Java, Go, C++,ESQL, etc.

Nice To Haves

Ability to evaluate and adopt tools pragmatically rather than by vendor alignment.

Responsibilities

Act as a guide and coach to product and engineering teams as they adopt AI across the Product Development Lifecycle (PDLC).
Work directly within teams to experiment and apply AI in real workflows.
Demonstrate what is possible with AI adoption.
Surface what works and codify repeatable patterns for others to adopt and scale.
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.
Integrate AI solutions into existing toolchains, such as IDEs, CI/CD pipelines, testing frameworks, and monitoring platforms.
Rapidly prototype to demonstrate value.
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 to ensure AI improvements are coordinated.
Redesign workflows to fully leverage AI capabilities.
Drive toward measurable results while maintaining quality, safety, and compliance.
Continuously refine approaches based on feedback and data.