Location

San Diego, CA. Not a remote role.

Position Summary

The Director, Strategy, Architecture and Solutions owns the front half of the enterprise AI lifecycle: from business intent to a designed, architected, and validated solution ready for Engineering and Delivery to build. This leader is the single point of accountability for what the function builds and why: how opportunities are surfaced and prioritized, how they are shaped into secure and scalable architectures, how they are proven through solution engineering and prototyping, how they are designed for adoption, and how their value is defined and tracked into the P&L.

The role sits between the business and the build. It holds enough technical depth to set architecture standards, decide build versus native platform capability (Salesforce, SAP, Databricks, AWS and Bedrock, the Illumina Digital Workforce platform), and hand Engineering a design that survives contact with production. Four capabilities report into it: Business Partners, Architecture, Solution Engineering, and Experience (UX/UI).

This is a leadership role, not a project management role. Success is measured by the quality of the portfolio entering build, the reuse of approved patterns, adoption of what ships, and the credibility of the AI function with senior business leaders. Vendor and commercial management sits with the Director, Governance, Trust and Vendors; this role is the primary internal customer of that function.

Responsibilities
Strategy and Business Partners

• Own the AI strategy and roadmap for the enterprise portfolio: where the function invests, in what sequence, and against which enterprise priorities, refreshed on an annual planning cadence with quarterly adjustments.
• Own the business partner model across all functions (Commercial, Finance, Global Operations, Manufacturing, HR, Service, Legal, Corporate) and act as the escalation point for enterprise AI demand.
• Run a structured intake and demand shaping process: capture, qualify, and prioritize AI opportunities against enterprise criteria rather than first come, first served.
• Build and defend the value case for every funded initiative: baseline, benefit hypothesis, quantification method, owner, and realization timeline.
• Partner with Finance to move benefits from slideware into the P&L, agreeing the treatment (cost avoidance, cost takeout, revenue enablement, capacity release) before build starts.
• Track realized value post go-live and report it on a fixed cadence to the VP, the CIO, and functional leadership.
• Feed recommendations into the AI Council and drive cross-business-unit reuse so the same capability is not built twice.
• Maintain the executive narrative: portfolio status, value delivered, risks, and asks, in a form ready for CIO and executive committee consumption.
• De-scope or stop initiatives that no longer clear the value bar, and make that case credibly to the sponsoring business leader.

Architecture

• Own the enterprise AI architecture: principles, reference architectures, reusable patterns, technology standards, and decision records for agents, AI products, data flows, integrations, and cloud services.
• Own the build versus native decision framework: when a capability belongs in Salesforce, SAP, Databricks, or another platform's native AI, and when it belongs on the Illumina Digital Workforce platform. Document and defend each decision.
• Define integration and connector standards so agents and solutions consume enterprise data and systems through approved, governed paths rather than point-to-point builds.
• Run the architecture review process for prioritized initiatives, with clear entry and exit criteria, and hold the right to block designs that do not meet standards.
• Ensure architectures support observability, resilience, secure data handling, responsible AI controls, performance, cost efficiency, and lifecycle management from the first design, in partnership with the Director, Governance, Trust and Vendors.
• Partner with the Data organization and Enterprise Architecture on data platform, master data, and integration decisions that AI solutions depend on.
• Maintain a pattern library and target the majority of new solutions being assembled from approved patterns rather than designed from scratch.

Solution Engineering

• Own the Discover and Design stages of the lifecycle: translate qualified opportunities into scoped, feasible, and architected solution designs.
• Lead feasibility assessment and rapid prototyping to prove technical viability, data readiness, and user fit before an initiative is funded for full build.
• Produce the build-ready package for each initiative: solution design, integration map, data requirements, non-functional requirements, acceptance criteria, and adoption plan.
• Own the handoff into Engineering and Delivery, and stay accountable for design intent through build, including design reviews, trade-off decisions, and production readiness sign-off.
• Decompose designs with Engineering into executable roadmaps and resolve trade-offs across speed, cost, security, scalability, and maintainability.
• Intervene directly on the highest-risk or highest-visibility solutions from proof of concept through first production release.
• Build engineering leverage by promoting modular components, shared services, and cross-business-unit reuse over one-off implementations.

Experience (UX/UI)

• Lead the UX function (UX lead and designers) covering research, interaction design, visual design, and content design for all AI products and agents.
• Establish and enforce a single enterprise AI design system: components, patterns, accessibility standards, and conversational and agentic interaction guidelines.
• Embed user research into the lifecycle: discovery interviews, usability testing, and post-launch behavioural analysis, with findings that change the product rather than decorate the deck.
• Set adoption as a design requirement. Own the definition of a usable release and hold the right to flag releases that will not be adopted.
• Partner with Engineering and Delivery so design intent survives implementation, and with Adoption and Change so training reflects the shipped experience.
• Ensure accessibility, inclusive design, and multi-region usability across the global user base.

Leadership and Cross-Functional

• Lead, coach, and develop a multi-geography team spanning business engagement, architecture, solution engineering, and design.
• Set objectives, performance standards, and career paths for direct reports and their teams.
• Operate as a peer to the Directors of Adoption and Change, Engineering and Delivery, and Governance, Trust and Vendors, resolving trade-offs without escalation wherever possible.
• Act as the primary internal customer of the vendor function: define the technical and solution requirements that drive partner selection, and provide input to quarterly business reviews with strategic partners.
• Represent the AI function to senior business stakeholders with clarity and candour, including when the answer is no or not yet.
• Contribute to annual planning, headcount strategy, and budget for the AI function.

Qualifications
Required

• Twelve or more years of experience across technology, digital, or transformation functions, including five or more years leading teams.
• Demonstrated ownership of an architecture, solutions, or business engagement function in a large, matrixed enterprise.
• Deep working fluency in enterprise AI architecture, including agentic systems, model providers, APIs, data and integration patterns, cloud platforms, security, and observability. Depth sufficient to challenge both business expectations and engineering proposals.
• Demonstrated experience making and defending build versus platform-native decisions across enterprise systems such as Salesforce, SAP, or Databricks.
• Track record of leading solution design and prototyping from opportunity through handoff to a production engineering team.
• Track record of building value cases that Finance accepted, and of reporting realized benefit after delivery.
• Experience owning or closely partnering with a product design or UX function.
• Executive communication skills, with evidence of influencing at VP and C-level.
• Bachelor's degree in computer science, engineering, information systems, or a related technology discipline.

Preferred

• Experience in genomics, life sciences, medical devices, or another regulated industry.
• Hands-on familiarity with AWS, Bedrock, Salesforce, SAP, Databricks, or Snowflake ecosystems.
• Exposure to AI governance frameworks, privacy, and responsible AI practices.
• MBA or equivalent advanced degree.
• Experience leading distributed teams across the United States, India, and Latin America.
• Typically requires a minimum of 18 years of related experience with a Bachelor's degree; or 15 years and a Master's degree; or a PhD with 12 years experience; or equivalent experience.

Competencies

• Architectural judgement: frames complex systems, makes explicit trade-offs, and establishes patterns that balance business value with security, scalability, reliability, and cost.
• Business translation: converts ambiguous business language into scoped, fundable, and technically executable solutions.
• Strategic sequencing: sees the portfolio as a whole, sequences investment, and resists building what a platform already provides.
• User obsession: treats adoption as the measure of success, not delivery.
• Directness: raises technical risk, weak value cases, and design compromises early and plainly.
• Portfolio discipline: says no, protects capacity, and favors reusable architecture over isolated solutions.

Success Measures

• Percentage of funded initiatives with a Finance-agreed value case at kickoff.
• Realized value versus committed value across the portfolio.
• Percentage of new solutions built from approved patterns and reference architectures.
• Rework rate after handoff to Engineering and Delivery, and cycle time from qualified opportunity to build-ready.
• Active adoption rates on delivered solutions at 30, 90, and 180 days.
• Reuse rate: solutions adopted by more than one business unit.
• Business stakeholder satisfaction with the AI function.

Working Relationships

• Internal: VP AI Enterprise and Transformation, CIO, Directors of Adoption and Change, Engineering and Delivery, and Governance, Trust and Vendors, plus Enterprise Architecture, software and data engineering leaders, Finance, Information Security, Privacy, Data, and business unit leadership.
• External: Model and platform providers, systems integrators, and design and research partners, in coordination with the vendor function.

#LI-HYBRID

The estimated base salary range for the Director of AI Strategy, Architecture and Solutions role based in the United States of America is: $205,100 - $307,700. Should the level or location of the role change during the hiring process, the applicable base pay range may be updated accordingly. The range reflects long-term growth in the role; therefore, most candidates are hired between the minimum and middle of the range. Placement depends on experience, skills, location, and internal equity. Additionally, all employees are eligible for one of our variable cash programs (bonus or commission) and eligible roles may receive equity as part of the compensation package. We offer a wide range of benefits as innovative as our work, including access to genomics sequencing, family planning, health/dental/vision, retirement benefits, and paid time off.