Chief AI Officer

- Own and drive the company-wide AI strategy, roadmap, and governance framework - Define the multi-year vision for AI adoption across service delivery, business development, and operations - Lead prioritization of AI initiatives against revenue, margin, and competitive differentiation outcomes - Report to the CEO on AI transformation progress; own the AI KPI scorecard

- Collaborate with CTO and Delivery Leaders to identify and build net-new AI-enabled service offerings that expand Fidus's addressable market - e.g., AI-assisted SI/PI analysis, automated design review, embedded AI in customer product development - Lead productization of Fidus's engineering expertise into scalable, repeatable AI-augmented deliverables - Partner with discipline leads (FPGA, SI/PI, Layout, Software, Hardware, ASIC) to embed AI tooling into delivery workflows - Establish IP strategy around proprietary AI methods and tooling developed internally

- Drive measurable adoption of AI tools across all teams - engineering, operations, and business development - Design and execute a structured change management program: enablement, training, and accountability - Define and own adoption KPIs: cycle time reduction, AI-assisted delivery percentage, proposal and BD efficiency - Champion a culture of experimentation while maintaining rigorous quality standards in safety-critical engineering environments

- Partner with IT to define and evolve Fidus's data architecture, governance, and quality standards as the foundation for AI capability - Collaborate with IT to build and maintain the data platform required to support AI model development, training, and deployment - Partner with IT and Legal to ensure data practices meet compliance requirements including ITAR, export control, and client NDA obligations - Work closely with IT and engineering leadership to instrument delivery processes for systematic data capture and to maintain data quality at the source

- Serve as Fidus's external AI authority - engaging clients, strategic partners, and industry forums - Advise clients on AI integration into their hardware, firmware, and systems product development programs - Support channel and business development by articulating Fidus's AI differentiation to silicon vendors, EDA partners, and the broader partner ecosystem

- Hire, develop, and manage the AI function - engineers, data scientists, and ML operations - Define the organizational model: centralized Center of Excellence or embedded discipline-level AI leads - Manage AI vendor and tool relationships - model providers, MLOps platforms, and data tooling - in coordination with IT procurement and security standards

- Services industry experience: Proven track record of driving AI adoption and transformation inside a services or professional services organization - not a product company. You understand the utilization model, the people dynamics of a billable delivery environment, and how to drive change without disrupting client commitments. - AI transformation leadership: 10+ years in technology leadership; 3+ years in a senior AI/ML leadership role with demonstrated organizational impact.

You have taken AI from concept to deployed, production-grade capability in an organization that did not start AI-native. - Deep data background: Hands-on expertise in data architecture, data governance, and ML pipeline development. Skilled at working across functional boundaries - able to engage credibly with both engineers and IT leadership, not just manage upward. - Product development experience: Demonstrated experience building AI-enabled products or productized service offerings, from ideation through commercialization. - Execution track record: History of executing on strategic roadmaps under ambiguity with measurable business outcomes - revenue, margin, efficiency.

You ship. - Executive communication: Able to translate AI strategy into board-level narrative and into day-to-day engineering direction. Equally fluent in both registers.

- Background in engineering services, EDA, semiconductor, or adjacent deep-tech industries - Familiarity with safety-critical or regulated engineering environments: A&D, medical, automotive - Experience in ITAR or export-controlled data environments - Prior experience in a company undergoing strategic transformation - high-growth, PE-backed, or M&A integration - Hands-on experience with LLMs, agentic AI frameworks, or AI applied to CAD/EDA and hardware design workflows