AI Adoption & Enablement Lead

The role involves AI tool and solution implementation in IT production teams, focusing on configuration, integration, building AI workflows, usage standards, technical documentation, and hands-on engineering support.

• Practical experience in software development environments as a developer, analyst, tester, DevOps, Scrum Master, Product Owner, or related role.
• Experience in deploying AI tools or solutions in IT teams including rollout, configuration, and integration with development processes.
• Good knowledge of Software Development Life Cycle (SDLC) and agile, cross-functional team work.
• Practical knowledge of AI tools supporting IT work, e.g., GitHub Copilot, Claude Code, Cursor, Codex, or M365 Copilot.
• Experience with Agentic SDLC: prompt engineering, Spec-Driven Development, workflow and AI agent design and construction.
• Understanding of generative AI, large language models (LLM), and AI agents capabilities and limitations in SDLC context.
• Ability to identify team needs and translate them into concrete AI solution implementations.
• Experience transferring knowledge via shared environment setups, workflow building, and technical sessions during rollouts.
• Ability to diagnose differences in team work practices using tool usage data and standardize practices organization-wide.
• Ability to build team autonomy in AI solution maintenance and development, including selecting AI practices and tools per team roles and tasks.
• Ability to create technical documentation, standards, playbooks, and knowledge base content.
• Capability to gather and interpret feedback and tool usage data for iterative solution improvements.
• High communication skills; ability to convey technical topics adapted to the audience.
• Ability to collaborate with diverse stakeholders: IT teams, leaders, AI Product Owner, AI Coaches, and AI Ambassadors.
• Ability to conduct technical workshop sessions with production teams as part of solution deployment.
• Independence, good work organization, and continuous AI development monitoring.

• Implement AI tools and solutions in IT production teams, including configuration and integration.
• Design and build AI workflows, usage standards, and technical documentation.
• Provide hands-on engineering support to teams using deployed AI solutions.
• Transfer knowledge through joint environment configuration, workflow building, and technical sessions.
• Diagnose team work differences based on tool usage data and unify practices and standards.
• Build team autonomy in maintaining and developing AI solutions.
• Create technical documentation, standards, playbooks, and knowledge base content.
• Collect and interpret feedback and usage data for iterative improvement.
• Collaborate with various stakeholders including IT teams, leaders, AI Product Owner, AI Coaches, and AI Ambassadors.
• Conduct technical workshops with development teams as part of solution implementation.