Head of Artificial Intelligence

Head, AI Centre of Excellence
Building portfolio-wide AI capability, reusable IP and measurable business outcomes
The Opportunity
STG Labs is building an AI Centre of Excellence to help portfolio companies reimagine products, modernize technology, adopt AI-native development practices, and improve business performance. The Head of AI CoE will lead and scale this capability across a diverse portfolio of B2B software companies. This is a hands-on executive leadership role at the intersection of technology, product management and delivery. The role owns the CoE's output, team, operating model and stakeholder relationships. Success requires the technical judgment to earn the respect of experienced engineers, the product instinct to identify high-value opportunities, and the leadership presence to convert ambition into production outcomes.
THE MANDATE
Build a trusted, high-velocity AI organization that repeatedly delivers measurable
outcomes and creates reusable capability across the portfolio.
Core Accountabilities
1. Lead and Scale the AI CoE
Own the strategy, operating model, delivery performance and continuous improvement of the AI CoE.
Lead a high-performing, high-velocity team delivering multiple concurrent initiatives across portfolio companies.
Recruit, develop, motivate and retain exceptional technical, product and delivery talent.
Establish clear roles, decision rights, performance objectives and accountability across the organization.
Create an environment that combines rapid experimentation with disciplined execution, quality and responsible AI governance.
2. Build Trusted PortCo Partnerships
Act as the primary interface between the AI CoE and portfolio company leadership, product management and technology teams.
Build trust with senior stakeholders, understand their business priorities, and align the CoE around problems that matter.
Influence without formal authority and navigate complex, matrixed stakeholder environments.
Ensure CoE initiatives are aligned to product roadmaps, operating priorities and the capacity required for adoption and deployment
3. Own End-to-End Delivery
Take accountability for the timing, quality and business impact of AI initiatives from discovery through production.
Govern a portfolio of complex projects using clear design principles, delivery standards, milestones and outcome measures.
Move teams rapidly from idea to validated prototype, while maintaining a clear path to production and scale.
Identify delivery risks early, resolve cross-functional dependencies and intervene directly when initiatives stall.
Establish and manage key performance indicators for a high-performing AI engineering and delivery organization.
4. Set Technical and Architectural Direction
Set the technical direction across active CoE workstreams and ensure solutions are secure, reliable, scalable and maintainable.
Bring sufficient technical depth to challenge assumptions, coach senior practitioners and make sound architecture and build-versus-buy decisions.
Define where AI-enabled automation can operate autonomously, where human oversight is required and where it should not be used.
Ensure interoperability and reuse are considered from the outset rather than added after delivery.
5. Connect Product Thinking to Engineering Execution
Partner with product leaders to frame customer and business problems before selecting solutions.
Help portfolio companies reimagine products and workflows rather than simply automate existing processes.
Ensure requirements, customer outcomes, commercial logic and engineering choices remain tightly connected.
Create mechanisms that convert successful prototypes into roadmap-aligned, production-ready capabilities.
6. Build Reusable IP and Portfolio Capability
Ensure each engagement creates value for the immediate project while contributing reusable methods, components and institutional knowledge.
Package repeatable approaches so they can be effectively adopted across multiple portfolio companies.
Establish a learning system that captures what works, what fails, and the conditions required for successful deployment.
Develop AI-native leaders and practitioners who strengthen both the CoE and participating portfolio companies.
7. Drive Business and Commercial Outcomes
Define success in terms of measurable business, customer, product and operating outcomes, not activity or demonstrations.
Balance speed, quality, cost and risk while operating in resource-constrained environments.
Partner with stakeholders to prioritize initiatives based on strategic value, feasibility and repeatability.
Communicate progress, risks, trade-offs and outcomes with clarity to executive stakeholders.
Candidate Profile
Experience
Significant leadership experience in product engineering, AI, data, platform engineering or technology transformation within a software or SaaS environment.
Proven track record of incubating, building and scaling high-performing technical organizations and complex delivery capabilities.
Experience owning architecture-level decisions as well as people, delivery and operational outcomes.
Demonstrated success leading technology transformation from legacy processes and systems to modern, scalable operating models.
Experience delivering complex products or platforms across distributed global teams and multiple time zones.
Proven ability to recruit, coach and retain strong technical talent and to earn credibility with experienced engineers quickly.
Experience working closely with product management and commercial leaders to align roadmaps, customer needs and strategic priorities.
Exposure to AI/ML and AI-native product development is strongly preferred; depth of technical leadership and judgment is essential.
Leadership Capabilities
Technical credibility: Can go deep enough to challenge architecture and engineering decisions without becoming the primary coder.
Builder mentality: Creates teams, systems and operating disciplines rather than relying on mature structures already in place.
Delivery ownership: Takes personal accountability for converting ideas into production outcomes.
Product and business judgment: Connects technology choices to customer value, commercial logic, and revenue or efficiency levers.
Systems thinking: Understands the full value chain, dependencies, and organizational consequences of what is built.
Trust and influence: Builds durable relationships and drives alignment across stakeholders without relying on hierarchy.
Judgment and quality bar: Evaluates AI-generated output rigorously and applies strong design, usability, and engineering judgment.
Executive communication: Communicates with clarity, structure, and gravitas; motivates teams and enables fast decisions.
What This Role Is Not
A pure research role focused on experimentation without delivery accountability.
A traditional offshore center-head role centered primarily on administration and people management.
A program management role that coordinates work while others own the outcomes.
A pure architecture role that provides guidance without responsibility for delivery and adoption.
A leader who needs a long ramp before engaging credibly with senior engineers and stakeholders.
Measures of Success
PortCo trust and demand - Portfolio companies actively engage the CoE and sponsor meaningful initiatives.
Business impact - Programs deliver measurable customer, product, revenue, cost, quality or productivity outcomes
Production conversion - Validated initiatives progress into adopted, production-ready capabilities
Delivery performance- The CoE meets clear commitments for speed, quality, risk and stakeholder communication
Reusable IP - Engagements produce repeatable methods, components and knowledge that can be leveraged elsewhere.
Talent flywheel - The CoE attracts strong talent and develops AI-native leaders across the portfolio.
Education
Bachelor's degree in engineering, computer science, business administration or a related discipline; a master's degree is preferred

Work arrangement
Hybrid