AI Enablement Technical Lead
TLDR
Own AI adoption across client teams, coaching technical leads and architects while embedding review discipline, QE practices, and reusable AI knowledge bases.
- 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.
About Electric Mind
Electric Mind is a fast-growing, AI-native advisory and digital engineering firm built for those who want to shape the future, not just watch it happen. We blend premium strategy expertise with cutting-edge AI-centric engineering to solve complex, meaningful problems for industry-leading clients.
We pride ourselves on a high-touch delivery model and a culture that values diverse talent, innovation, and true client partnership — creating an environment where your ideas matter and your impact is visible.
If you’re looking for a place where you can grow fast, collaborate with exceptional teammates, and help build a company scaling its capabilities and global footprint at speed — Electric Mind is the place to ignite your career. The future is bright!
For more info on Electric Mind, check out our Careers Page and Instagram.
Electric Mind is committed to diversity in the workplace. We are an inclusive employer and welcome and encourage applications from all qualified candidates. Applicants’ needs will be accommodated during our recruitment and selection process so please advise us if you require accommodation.
Electric Mind is an AI-native advisory and digital engineering firm that combines premium strategy expertise with advanced AI solutions. We serve industry-leading clients by addressing complex challenges through a high-touch delivery model that emphasizes innovation and collaboration.