Office of AI Leadership
- Define and execute enterprise AI strategy across internal productivity tools, customer offerings, and managed services
- Operate the AI platform layer, including model management, tooling, data pipelines, guardrails, and evaluation frameworks
- Drive organization-wide AI adoption through fluency programs, training, playbooks, and industry-specific solution accelerators
- Establish comprehensive AI governance covering risk management, compliance, security, safety, and model lifecycle management
- Own AI cost modeling and unit economics in partnership with Finance and Operations teams
- Manage GPU and CPU capacity strategy to optimize performance and cost
AI Strategy & Business Impact
- Own the AI platform roadmap, model portfolio, and evaluation and monitoring approaches
- Drive AI solution patterns for priority industries and embed AI capabilities into every product and service offering
- Measure and report AI value creation, including revenue impact, margin improvement, productivity gains, quality enhancements, and risk reduction
- Lead co-innovation initiatives with key technology partners and AI vendors
- Coordinate AI go-to-market strategy with Product and Sales organizations
AI Governance & Responsible AI
- Set comprehensive policies for responsible AI including ethical use, bias mitigation, and fairness
- Establish data usage policies, privacy protections, and regulatory compliance frameworks
- Define AI safety standards and incident response protocols
- Create transparency and explainability requirements for AI systems
- Monitor and enforce adherence to AI governance policies across the organization
CTO Collaboration & Platform Integration
- Ensure AI platform standards align with overall technology architecture established by the CTO
- Obtain joint approval with CTO for AI architectures that impact core platform decisions or risk posture
- Participate in quarterly technology and AI strategy reviews with integrated roadmaps
- Co-lead monthly architecture and model governance councils
- Coordinate on platform reliability, security, and cost optimization initiatives
Key Performance Indicators
- AI-attributed revenue and pipeline contribution
- AI-driven productivity improvements and cost savings
- AI adoption metrics across internal teams and customer base
- Model quality, performance, and safety scores
- AI platform reliability and uptime
- Cost per AI inference or transaction
- Compliance with AI governance policies and regulations
- Partner ecosystem engagement and co-innovation outcomes
- Customer satisfaction with AI-powered solutions
Technical Expertise
- Deep expertise in AI/ML technologies, large language models, and generative AI
- Strong understanding of AI platform architecture, MLOps, and model lifecycle management
- Knowledge of AI safety, bias mitigation, explainability, and responsible AI practices
- Familiarity with cloud infrastructure, data engineering, and modern software development practices
- Understanding of AI regulatory landscape and compliance requirements
Leadership Capabilities
- Strategic thinker who can translate AI capabilities into business value and competitive advantage
- Exceptional communication skills with the ability to educate and influence at all organizational levels
- Proven ability to drive adoption and change management across large organizations
- Experience building and leading multidisciplinary AI teams, including researchers, engineers, and data scientists
- Track record of partner management and ecosystem development
- Strong business acumen withan understanding of go-to-market and monetization strategies
Required Qualifications
- Bachelorโs degree in Computer Science, Engineering, Mathematics, or related technical field required
- Advanced degree (Master's or PhD) in AI, Machine Learning, Computer Science, or related field strongly preferred
- MBA or equivalent business education a plus
- 12+ years of progressive technology and AI leadership experience with at least 5 years in senior executive roles
- Proven track record building and scaling AI/ML platforms, products, or practices in enterprise environments
- Experience driving AI strategy that delivers measurable business outcomes and revenue impact
- History of establishing AI governance frameworks and responsible AI programs
- Experience managing large-scale AI infrastructure, model operations, and GPU/compute resources
