Electric Mind – AI Enablement Technical Lead

About the Role

Act as the accountable Electric Mind (EM) technical leader across a number of client teams, for a large scale AI Enablement program. The Tech Lead owns the adoption approach to Electric Mind’s AI-enablement delivery method and coaches Client Tech Leads, QE Leads, and Architects as required so the AI adoption practices across the different client teams remain consistent . This role is distinct from a typical Tech Lead role: the Electric Mind Tech Lead owns the AI adoption across multiple client team, whereas a typical tech lead owns the technical delivery. Our Technical Leads will work closely with the Program Engineering Lead to foster AI adoption and will escalate any issues or blockers to adoption that fall outside of their remit to the Engineering Lead.

Act as the single point of Electric Mind technical accountability across its assigned pods — the owner of technical direction and decisions for how the AI-enabled delivery method is adopted technically. As the technical accountability point, the Tech Lead must be able to:

• Own AI adoption for the technical aspects of the Software Delivery Lifecycle : this comprises, but may not be limited to, how AI-assisted engineering, code, and QE practices are introduced, sequenced, and embedded across the client teams.
• Coach Client Tech Leads, QE Leads, and Architects as required, building their capability to foster adoption of AI enablement best practices within their own teams.
• Reinforce a review-based culture and make sure the processes around solution design review, code review, QE plan and test review are strong and disciplined, focused on correctness against acceptance criteria rather than style. As AI compresses authoring, review becomes the real bottleneck, and review discipline is frequently a pre-existing team weakness that must be addressed.
• Own accountability for the integration of the QE approach into the AI enabled lifecycle. E.g. where QE enters the delivery flow, how it QE activities connect to solution design and implementation, and how AI-authored tests are used as the mechanism for instilling trust in AI-generated functionality.
• Own the approach to the build out of the technical artifacts that will be part of each solution’s AI knowledge-base. Ensure that these artifacts support implementation readiness, portability, and reusability so the team’s technical work can be reused rather than remaining a one-off local solution.
• Ensure AI-generated technical outputs respect engineering constraints, architecture, coding standards, repository structure, test requirements, and enterprise stack limits across its assigned pods.
• Make sure the right technical role owner (developer, QE, or architect) is accountable for technical, QE, architecture, or tooling issues, and step in where technical ownership is unclear.
• Surface AI Enablement patterns across assigned teams— such as recurring technical risks, architecture gaps, and inconsistent engineering or QE practice — so that they can be resolved across the program rather than in isolation.
• Escalate to the Engineering Lead when factors outside of the role’s remit block or impede the smooth adoption of AI Enablement practices.

Single point of Electric Mind technical accountability for AI Enablement across assigned client teams.

Own the Electric Mind approach to AI-Enablement across technical disciplines (Dev, QE, Architecture, ...).

Coaching of Client Tech Leads, QE Leads, and Architects.

Instill a review-based culture and provide guidance on discipline and practices.

Integration of QE discipline into the AI Enabled lifecycle.

AI knowledge-base readiness, portability, and reusability.

Reporting of patterns (good and bad) across its assigned teams and escalation to the Engineering Lead.

Senior technical leadership experience, capable of being the single point of technical accountability across multiple teams.

Strong understanding of how to apply AI Enablement methods for technology delivery teams.

Strong software engineering and architecture literacy . Familiarity with technical standards and industry best practices.

Ability to own and drive a technical adoption approach for a new (AI enabled) engineering method.

Working knowledge of AI-assisted engineering, agentic development harnesses, model behaviour, common AI failure modes and skills standards

Ability to coach senior technical roles — Tech Leads, QE Leads, and Architects — not just individual developers.

Experience in disciplined code reviews and documentation review practices.

Ability to distinguish a technical or architecture problem from a process, requirements, or delivery-management problem.

Comfort using AI tools to complete job functions.

Familiarity with SpecDriven development solutions a plus.