Sr Director AI Enterprise Engineering

Pacific Life•Newport Beach, CA

1d•$203,760 - $249,040•Onsite

About The Position

Providing for loved ones, planning rewarding retirements, saving enough for whatever lies ahead – our policyholders count on us to be there when it matters most. It’s a big ask, but it’s one that we have the power to deliver when we work together. We collaborate and innovate – pushing one another to transform not just Pacific Life, but the entire industry for the better. Why? Because it’s the right thing to do. Pacific Life is more than a job, it’s a career with purpose. It’s a career where you have the support, balance, and resources to make a positive impact on the future – including your own. We’re actively seeking a talented Sr Director of AI Engineering to join our Engineering Excellence team in Newport Beach, CA. As a Sr Director of AI Engineering, you’ll move Pacific Life, and your career, forward by advancing PL engineering through leveraging AI toward agentic adoption where the work supports it or is required. This role owns delivery across four capability areas for Engineering Excellence (Code & Delivery, Engineering in Test, Reliability & Telemetry, Security & Compliance), ensuring that AI capabilities are operationalized from experimentation into secure, production-grade systems. This role operates and drives enterprise adoption of AI-enabled engineering practices, platform standardization, and governance. Scope Portfolio: Enterprise AI engineering platforms including CI/CD, AI-assisted development, test generation and quality intelligence, observability/AIOps, and continuous security and compliance Operating Model: Product based delivery with shared artifacts (Build Register) and continuous feedback loops (Spec ↔ Build ↔ Apply) Enterprise Impact: Platforms consumed broadly across engineering to drive productivity, quality, and reliability

Requirements

Engineering leadership: Proven track record leading multi-team platform or AI engineering organizations at Director+ scope.
Domain depth and breadth: Deep expertise in at least two domains, such as Developer Experience Engineering, Software Engineering in Test, SRE/Observability, or Application Security, with working fluency across the others.
Production AI delivery: Hands-on experience building and scaling production AI/ML systems, including LLMs, ML pipelines, or AI platforms.
Cloud-native architecture: Strong understanding of cloud-native architecture such as Azure, AWS, or GCP and single or multi cloud design and deployment
AI lifecycle governance: Experience implementing MLOps, LLMOps, and AI lifecycle governance practices.
Security, privacy, and compliance: Strong understanding of secure SDLC, data privacy, and compliance requirements as well as fluency in AI and LLM emerging security challenges.
Technical risk leadership: Ability to operate as a peer to security leadership and own technical risk conversations.
Minimum 20+ years of engineering experience, including at least 8 years leading teams at Director+ scope.

Nice To Haves

Enterprise AI platform experience: Experience building enterprise AI platforms or scaling enterprise engineering organizations.
Responsible AI and regulatory fluency: Familiarity with Responsible AI frameworks, such as NIST AI RMF, and related regulatory expectations.
Agentic architecture exposure: Experience with multi-agent architectures, orchestration layers, or model routing.
Regulated industry experience: Experience operating in regulated industries, such as financial services, insurance, or healthcare.
Platform adoption leadership: Experience managing platform adoption and internal developer experience programs.

Responsibilities

Define and execute the enterprise AI Engineering Build roadmap, ensuring priorities are clearly aligned to business outcomes, platform adoption goals, and enterprise engineering needs.
Build portfolio management: Own the Build Register as the system of record for AI engineering platforms, tools, components, and delivery commitments.
Planning and prioritization: Operate Spec ↔ Build planning loops, using adoption signals and enterprise feedback to shape roadmap priorities.
AI platform delivery: Lead delivery of production-ready AI platform capabilities, including LLM integration, RAG systems, and agentic workflows.
MLOps and LLMOps practices: Establish production-grade MLOps and LLMOps capabilities, including CI/CD, model lifecycle governance, monitoring, and evaluation.
Enterprise standards and Responsible AI: Ensure AI engineering platforms meet enterprise expectations for security, privacy, compliance, and Responsible AI.
Secure-by-default engineering: Embed secure-by-default practices across AI and engineering workflows, including application security, threat modeling, and policy enforcement.
Governance and risk partnership: Partner with CISO and risk leadership to define governance expectations, strengthen control alignment, and manage high-risk releases.
Cross-functional alignment: Drive alignment across engineering, product, platform, and security teams to ensure shared priorities, clear decision-making, and coordinated execution.
Organization building: Build and scale a high-performing engineering organization through hiring, rotation planning, leadership development, and capability growth.
Dependency management: Resolve cross-team dependencies through lateral alignment, shared accountability, and proactive escalation management.