Director of AI

We are seeking a visionary and execution-focused Director of Artificial Intelligence to lead and scale our enterprise AI transformation strategy across all technology functions. This role will own the AI portfolio, roadmap, governance framework, AI platform strategy, AI operations, AI-enabled engineering transformation, and AI product innovation initiatives. The director will work closely with engineering, quality, architecture, operations, hardware engineering, computer vision, and executive leadership to transform AI from a collection of successful initiatives into a standardized and measurable operating model. This aligns with the organization and next phase of AI maturity: moving from adoption and experimentation toward industrialization, governance, platform consolidation, measurement, and enterprise-wide scale. The successful candidate will combine deep AI expertise, strong engineering leadership, program management excellence, and business acumen to accelerate productivity, innovation, and competitive advantage through AI.

Responsibilities:

• Establish a unified CTO AI strategy and operating model.
• Create AI governance, standards, and a compliance framework.
• Deliver an enterprise AI portfolio with measurable business outcomes.
• Standardize AI adoption measurement across departments.
• Expand AI-enabled engineering capabilities and agentic workflows.
• Implement executive AI dashboards and reporting mechanisms.
• Demonstrate measurable productivity, quality, and operational improvements attributable to AI initiatives.
• Build a sustainable AI organization capable of scaling AI across all technology functions.

AI Strategy and Leadership:

• Define and execute the enterprise AI strategy across the CTO organization.
• Develop a multi-year AI roadmap aligned with business and technology objectives.
• Identify, prioritize, and govern AI initiatives across engineering, operations, product development, and innovation teams.
• Serve as the senior AI advisor to executive leadership and ExCo.
• Drive adoption of AI as a core capability within the engineering operating model.

AI Transformation and Organizational Adoption:

• Scale AI adoption across software engineering, quality engineering, architecture, SRE/L3 hardware engineering, computer vision, and R& D functions.
• Establish AI transformation plans and maturity targets for each department.
• Drive AI-enabled productivity improvements in software delivery, testing, architecture design, operational support, documentation, analytics, and engineering workflows.
• Build and lead communities of practice that accelerate AI adoption and innovation.

AI Engineering and Platform Strategy:

• Define the enterprise architecture for AI platforms, agents, MCPs (Model Context Providers), knowledge systems, and engineering automation capabilities.
• Lead the development of reusable AI frameworks, AI services, AI portals, and engineering productivity platforms.
• Drive the adoption of AI tooling, including coding assistants, agentic development platforms, test automation agents, and AI-powered development workflows.
• Establish architectural standards, best practices, and technical guardrails for AI solutions.

Agentic Engineering and Automation:

• Lead the organization's transition from AI-assisted work toward AI-agent-driven execution.
• Identify opportunities for autonomous agents across software development, testing, DevOps, support, documentation, and operational workflows.
• Sponsor initiatives involving AI-generated testing, automated validation, AI-driven regression testing, engineering copilots, and agent orchestration platforms.
• Establish metrics to measure AI-generated outcomes and productivity improvements.

AI Governance, Risk, and Compliance:

• Own the enterprise AI governance framework.
• Establish AI policies, standards, guardrails, security controls, and acceptable-use models.
• Ensure responsible AI practices covering transparency, privacy, intellectual property protection, auditability, and regulatory requirements.
• Partner with security, legal, compliance, and leadership teams to manage AI-related risks.
• Lead AI risk assessments and mitigation strategies.

AI Operations and FinOps:

• Establish AI operational governance and AI platform lifecycle management.
• Own AI usage monitoring, tooling optimization, cost management, and AI ROI reporting. Discussions within the AI governance team emphasize measurement of adoption, ROI, quotas, and tool governance.
• Develop KPI frameworks measuring adoption, productivity, business impact, cost, and risk.
• Drive AI portfolio planning and investment prioritization.

Executive Reporting and Business Value Realization:

• Create executive dashboards and AI maturity assessments for senior leadership and ExCo. AI adoption reporting and maturity tracking are already key organizational activities.
• Communicate AI strategy, risks, investment requirements, and realized value.
• Develop business cases and investment proposals for new AI initiatives.
• Ensure measurable value realization from AI investments.

Team Leadership:

• Build and lead a high-performing AI organization consisting of AI engineers, AI architects, AI operations specialists, data and knowledge engineers, AI product managers, and AI governance specialists.
• Coach technical leaders and management teams on AI adoption and transformation.
• Create an AI-first culture focused on innovation, experimentation, and measurable outcomes.

Requirements:

• Bachelor's degree in computer science, engineering, data science, or artificial intelligence, or equivalent experience.
• Master's degree preferred.
• 10+ years of technology leadership experience.
• 5+ years leading enterprise AI, machine learning, data science, or intelligent automation initiatives.
• Experience managing large cross-functional transformation programs.
• Experience defining and scaling enterprise AI governance frameworks.
• Proven success delivering measurable business outcomes through AI and automation.

Technical Expertise:

• Strong knowledge of generative AI, large language models (LLMs), agentic AI architectures, prompt engineering, model orchestration, vector databases, retrieval-augmented generation (RAG), MCP architectures, AI security and governance, MLOps / LLMOps, and cloud AI platforms (Azure, AWS, and GCP).
• Leadership Competencies: Strategic Thinking, Executive Presence, Technology Vision, Organizational Influence, Change Leadership, Program Execution Excellence, Innovation Management, Data-Driven Decision-Making, Stakeholder Management, and Talent Development.