AI Coach

A Job Title: AI Coach – Engineering Productivity & SDLC Transformation Location: Denver, CO (Second Preference : Charlotte, St Louis) Onsite Role Overview The AI Coach will drive AI adoption across a backend engineering department focused on Java and Oracle DB API development. This role is responsible for assessing the current state of engineering workflows, identifying high‑impact AI use cases across the SDLC, and enabling measurable productivity improvements using approved AI tools such as Amazon Q, GitHub Copilot (Duo), and Kiro. The role is outcome‑driven, with a strong focus on engineering efficiency, code quality, delivery speed, and developer experience, rather than pure tool enablement. Key Responsibilities 1. Current State Assessment • Assess existing SDLC processes across requirements, design, development, testing, deployment, and support. • Analyze current engineering productivity, pain points, bottlenecks, and manual effort areas • Understand team maturity, coding practices, CI/CD usage, and quality controls. 1. AI Use Case Identification & Enablement • Identify and prioritize AI-driven use cases to improve SDLC productivity, such as: Code generation and refactoring, Unit and integration test creation, Code review acceleration, SQL optimization and query analysis, Documentation generation, Defect analysis and root cause insights • Map use cases explicitly to Amazon Q, GitHub Copilot (Duo), and Kiro, ensuring practical and compliant usage. • Define where AI should augment engineers, not replace engineering judgment. 1. Metrics, KPIs & Outcome Definition · Define clear productivity metrics and KPIs to measure improvement, such as but not limited to: Cycle time reduction, Code churn and rework reduction, Test coverage improvements, Defect leakage reduction, Developer throughput and focus time · Establish baseline metrics and track progress post‑AI adoption. · Regularly report outcomes to engineering leadership with data‑backed insights. 1. Coaching & Enablement · Act as a hands‑on AI coach for engineers, tech leads, and managers. · Conduct onsite workshops, hands‑on sessions, and office hours. · Create lightweight best practices, guardrails, and usage patterns for AI tools. · Drive adoption through influence, coaching, and demonstrable results rather than mandates. 1. Governance & Responsible AI Adoption • Ensure AI usage aligns with enterprise security, compliance, and IP guidelines. • Define what should and should not be used with AI tools. • Partner with architecture, security, and leadership teams as needed. Required Skills: • Strong experience in Java backend development and Oracle DB. • Solid understanding of enterprise SDLC. • Hands‑on experience with AI‑assisted dev tools (Amazon Q, Copilot, Kiro). • Ability to define and track engineering productivity metrics.