AI Delivery & Quality LeadThe AI Delivery & Quality Lead is responsible for improving the effectiveness, rigor, quality, and business impact of AI delivery across the organization.
This role serves as the bridge between business objectives, AI solution development, adoption, value realization, and technical quality assurance. The successful candidate will help AI teams move beyond experimentation and technical outputs toward trusted, measurable business outcomes.
The individual will establish delivery standards, evaluation frameworks, quality controls, and governance practices that improve the integrity of AI solutions throughout their lifecycle. They will coach practitioners, challenge weak assumptions, identify technical and delivery risks, and ensure AI solutions are grounded in business needs, credible data, appropriate evaluation methods, and measurable results.
Success will be measured by adoption, business impact, technical quality, trustworthiness, and realized value rather than model development activity, experimentation volume, prototype creation, or technical complexity.
Key ResponsibilitiesAI Delivery Leadership
Lead AI initiatives from problem definition through adoption and value realization.
Establish and enforce AI delivery standards across the organization.
Create repeatable delivery practices for problem framing, experimentation, evaluation, deployment, adoption, and value measurement.
Ensure AI initiatives remain aligned with business priorities and operational outcomes.
Help teams distinguish between research activity, prototype development, technical progress, and realized business value.
Improve overall delivery maturity across AI teams, projects, and business functions.
AI Quality and Technical Assurance
Establish standards for AI solution evaluation, validation, testing, and production readiness.
Review proposed AI solutions for technical soundness and fitness for purpose.
Challenge teams on model selection, evaluation methods, experimentation approaches, and technical assumptions.
Identify and mitigate common AI and machine learning risks including:
Data leakage
Inappropriate training and testing methodologies
Sampling bias
Weak evaluation frameworks
Unsupported performance claims
Overfitting
Poor baseline comparisons
Hallucination risks
Reliability and robustness concerns
Define acceptance criteria for AI solutions before deployment.
Create review processes that improve confidence in AI outcomes and reported results.
Act as an independent voice of quality and rigor across the AI portfolio.
Problem Framing and Solution Design
Partner with business stakeholders to understand operational challenges, decision points, workflows, and desired outcomes.
Ensure initiatives are solving meaningful business problems rather than applying AI for its own sake.
Guide teams in developing hypotheses, success criteria, and evaluation strategies.
Ensure solution designs appropriately balance business value, technical feasibility, risk, cost, and maintainability.
Delivery Governance and Standards
Establish consistent methodologies for AI delivery across multiple teams.
Define quality gates, review processes, and readiness assessments.
Create standards for documentation, evaluation, testing, deployment, and measurement.
Improve transparency and consistency in AI project reporting.
Develop practical approaches for AI governance, risk management, and responsible AI adoption.
Business Adoption and Value Realization
Ensure solutions are adopted and integrated into operational workflows.
Define and measure outcome-based success criteria.
Validate claimed business benefits and value realization.
Improve confidence in AI investments through rigorous measurement and reporting.
Partner with business leaders to ensure AI initiatives generate measurable operational and financial outcomes.
Coaching and Organizational Capability Building
Coach practitioners, product teams, and business stakeholders on AI delivery best practices.
Improve organizational understanding of AI quality, evaluation, and delivery discipline.
Mentor teams in problem framing, experimentation, measurement, and adoption strategies.
Help establish a culture of evidence-based decision making and continuous improvement.
Required Qualifications- 10+ years of experience leading technology, analytics, AI, digital, product, or transformation initiatives.
- Demonstrated experience leading AI, machine learning, advanced analytics, or intelligent automation solutions from concept through production adoption.
- Strong understanding of AI and machine learning delivery practices, evaluation methodologies, and production deployment considerations.
- Experience establishing organizational standards, governance processes, quality controls, or delivery frameworks.
- Ability to identify common AI and machine learning failure modes including data leakage, weak evaluation design, overfitting, poor baselines, sampling bias, and unsupported performance claims.
- Experience assessing the quality and credibility of AI solutions and technical recommendations.
- Strong business acumen and ability to connect technical work to operational and financial outcomes.
- Experience coaching teams and improving delivery discipline.
- Excellent communication, facilitation, and stakeholder management skills.
- Demonstrated ability to challenge assumptions, influence senior leaders, and drive accountability.
- Experience operating effectively in environments with significant ambiguity and evolving requirements.
Preferred Qualifications- Experience leading AI product delivery organizations.
- Experience establishing AI governance, quality assurance, evaluation, or model review processes.
- Familiarity with machine learning development lifecycles and MLOps practices.
- Experience with generative AI, agentic AI, retrieval systems, enterprise AI platforms, and AI-enabled decision support solutions.
- Consulting, product leadership, delivery leadership, or transformation leadership experience.
- Experience working with cross-functional teams including business leaders, engineers, architects, product owners, operators, and executives.
- Experience in energy, manufacturing, logistics, industrial operations, automotive, aviation, or other asset-intensive industries.
“ TalentBridge employees are eligible for many benefit offerings such as medical, dental, vision, life insurance, short term disability, 401(k) and holiday pay!”