AI Enablement Senior Engineer

What You’ll Do

Act as the contact person for coaching and questions related to AI adoption and AI enablement in the software delivery lifecycle, from technical client team members which the team cannot resolve themselves through the training they will have been provided. This responsibility will cover multiple client teams. The Senior Engineer must be able to:
Mentor technical client team members on how to validate plans and code generated by AI, against intended story outcomes .
Coach client team members on focusing on delivery rather than detouring into AI skills changes mid-flight capturing friction for later skill change iterations.


Support client team members in instilling a strong review ethic across code, tests, PRs and other AI generated artifacts requiring HITL (Human In The Loop) reviews. Guide the teams in focusing reviews on content and alignment with acceptance criteria rather than style.
Help diagnose experiences and environment friction in real usage (skill discovery, workspace confusion, command generation, dependencies, tests, branch and PR flow, IDE/tool timeouts, request limits, CLI setup). Treat environment friction as separate from AI-method friction — surface anything being worked around rather than fixed, and either help fix it or escalate it so it is not silently absorbed as “AI doesn’t work.”
Support the  integration of tooling (scripting, MCP, ...) by client teams  needed by client teams to facilitate the automation of activities between AI and existing enterprise solutions (e.g. Jira & Confluence integrations).
Support the creation of the AI knowledge base and related AI supported reverse-engineering activities as required by various client teams.
Support the setup of the AI enabled project environment (repositories, skills, agents, configuration files, etc) as needed by each client solution’s or client team’s specific requirements and context.
Coach client team technical staff (Developers, Quality Engineers, ...) how to  validate that AI-generated output respects each team’s engineering constraints, coding guidelines, test requirements, enterprise standards, etc.
Work with the indicated team members to capture and improve AI skills and agentic capabilities, reinforcing that skills encode team knowledge .
Escalate to the AI Enablement Technical Lead when technical decisions, tooling blockers, access, or capacity issues fall outside the role’s remit, and surface recurring friction across your assigned client teams rather than solving these  in isolation.

Key Deliverables

Guidance and mentoring for  technical client team members (Developers and Quality Engineers mainly)
Support  deep-dive sessions and story walkthroughs across its assigned client teams whereas these relate to the team's ability to further AI enablement.
Creation and supporting the creation of guidance for technical personnel such as walkthrough notes, step by step job aids, etc..
Coaching in AI enabling techniques and practices as per the program’s standards and approach.
Logging and sharing/escalating experience and environment/tooling-friction findings.
Support for  AI Knowledge-base creation and reverse-engineering through in person assistance or the delivery of guidance artifacts (documentation, job aids, ...).
Capture of AI skill improvement requirements across client teams, and collaboration with program team to elicit these improvements.
Scripting or support of scripting for enterprise tool integration (e.g. Jira, Confluence, ...).
Developer-friction pattern reporting across its assigned client teams.

What You’ll Bring

Strong software delivery experience across various technology stacks using industry best practices (versioning, PR reviews, solution design, code, ...).
Understanding of how to apply AI capabilities such as SpecDriven development to support AI enablement of the Software Delivery Lifecycle.
Ability to run practical, hands-on enablement sessions with technical personnel, and support client team members in running the same.
Familiarity with AI-assisted coding tools, agentic IDE workflows, skills, model behaviour, and common AI failure modes.
Ability to diagnose friction across setup, workspace structure, command execution, dependencies, tests, and source-control flows, and to distinguish environment friction from AI friction.
Working knowledge of AI-assisted Quality Engineering methods (e.g. test generation, execution, defect review) .
Good communication skills and the ability to coach technical personnel who may be skeptical of AI-generated output or attached to existing coding habits.
Enough architectural and code literacy to distinguish an AI skill/prompting problem from a specification quality, context, setup, or codebase problem.
Familiarity with SpecDriven development solutions is a plus.
Ability to help pods test and validate the story-development / quick-dev skill chain against realistic stories, including UI framework, API, and other story flavours as they become available.
Working knowledge of the A.I.D.E Framework.
Comfort using AI tools to complete job functions.