AI Dev Coach

The AI Coach - DevOps Practice is responsible for driving the adoption and effective utilization of AI technologies across software engineering and DevOps teams. This role combines technical expertise, coaching, consulting, and change management capabilities to help engineering organizations improve productivity, software quality, and delivery performance through AI-enabled ways of working.

The AI Coach acts as a trusted advisor to developers, DevOps engineers, architects, engineering managers, and technology leaders, helping teams identify, implement, govern, and scale AI solutions throughout the Software Development Lifecycle (SDLC). The role focuses on maximizing business value from AI investments by promoting best practices, measuring outcomes, and ensuring responsible and secure AI adoption.

Responsibilities:

AI Adoption & Enablement

• Lead AI adoption initiatives across cross-functional software engineering, DevOps, platform engineering, QA, and product teams.

• Conduct training sessions, workshops, office hours, and hands-on coaching to improve practical usage of AI-powered development tools.

• Mentor engineers and technical leaders in effective use of GitHub Copilot, Claude Code, Cursor, and other AI-assisted development technologies.

• Promote AI literacy and help teams integrate AI into their daily ways of working.

AI-Powered SDLC Transformation

• Drive implementation of AI solutions across the Software Development Lifecycle (SDLC), from requirements analysis through development, testing, deployment, and operations.

• Assess team needs and identify high-value use cases for AI adoption within engineering and DevOps practices.

• Define standards, usage guidelines, and governance frameworks for responsible AI utilization.

• Support scaling of successful AI initiatives across multiple teams and delivery organizations.

Measurement & Value Realization

• Define and monitor KPIs, OKRs, and success metrics related to AI adoption and engineering productivity.

• Measure business impact and ROI of AI initiatives across software delivery and DevOps organizations.

• Produce executive-level reporting and insights on adoption progress, efficiency gains, and value realization.

Requirements:

Must-Have Qualifications

• Minimum 5 years of experience in a technical role such as Software Developer, DevOps Engineer, Platform Engineer, QA Automation Engineer, SRE, or similar.

• Hands-on experience with AI-powered engineering tools, including GitHub Copilot, Claude Code, Cursor, Microsoft 365 Copilot, or equivalent solutions.

• Strong understanding of modern Software Development Lifecycle (SDLC) practices and DevOps methodologies.

• Ability to lead training sessions, workshops, and coaching activities for technical and non-technical audiences.

• Deep understanding of the current AI ecosystem, including LLMs, AI-assisted coding, content generation, agentic workflows, and automation.

• Experience designing effective prompts, AI workflows, agents, and reusable AI solutions.

• Knowledge of AI governance, security, compliance, and responsible AI practices.

• Experience evaluating AI tools and platforms, considering quality, cost, risks, security, and regulatory requirements.

• Analytical mindset with the ability to identify, measure, and prioritize AI use cases that deliver business value.

• Strong passion for continuous learning and staying current with emerging AI technologies and industry trends.

Nice-to-Have Qualifications

• Experience working in large enterprise environments, preferably within regulated industries such as financial services, banking, insurance, or healthcare.

• Experience driving AI adoption or digital transformation initiatives across multiple teams or business units.

• Knowledge of AI application architectures and frameworks, including RAG, LangGraph, LangChain, AI agents, and multi-agent systems.

• Familiarity with Spec-Driven Development and AI-assisted software development methodologies.

• Experience developing internal standards, best practices, playbooks, and governance frameworks.

• Understanding of FinOps principles and optimization of AI-related costs.

• Experience implementing AI solutions within engineering, DevOps, platform engineering, or cloud environments.

• Public speaking experience, community leadership, conference presentations, or internal technical evangelism activities.

• Relevant certifications in cloud, DevOps, AI, or software engineering domains.

Key Competencies

• Coaching and mentoring

• Facilitation and workshop leadership

• Influencing and change management

• Strategic thinking

• Data-driven decision making

• Strong ownership and execution skills

• Curiosity and experimentation mindset