Electric Mind – AI Enablement Technical Lead
Quick Summary
At Electric Mind, Engineering is where strategy meets action. Our team helps organizations cut through complexity—aligning business ambition with technology execution to unlock real, lasting change.
About the Role
~5 min readAct 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.
- 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.
Location & Eligibility
Listing Details
- Posted
- August 4, 2026
- First seen
- August 4, 2026
- Last seen
- August 5, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 62%
- Scored at
- August 4, 2026
Signal breakdown
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