Senior AI Engineer

Job Title

Senior AI Engineer

Job Description

We're looking for a Senior AI Engineer at Pacific Life Re.

The Senior AI Engineer designs, builds, and pilots AI‑enabled solutions that support PL Re business teams through experimentation, rapid prototyping, and value‑led innovation. Operating within the Business & AI team, the role translates business problems into secure, scalable, cloud‑hosted AI solutions suitable for a regulated reinsurance environment.

The role combines hands‑on AI and full‑stack development with strong engineering judgement, ensuring alignment with PL Re engineering standards, enterprise architecture, and security controls. A key focus is maintaining awareness of emerging AI technologies and applying relevant advances pragmatically.

The role works closely with the Global Experience Analysis (Global EA) engineering team to accelerate AI adoption while ensuring solutions can transition from experimentation into enterprise platforms where appropriate.

Key Responsibilities

AI Solution Design & Delivery

• Design, build, and pilot AI‑enabled solutions that address PL Re business problems through experimentation and rapid prototyping.

• Translate business use cases into secure, scalable, cloud‑hosted AI solutions aligned to enterprise standards.

• Deliver hands‑on full‑stack and AI development, producing solutions capable of transition into strategic or BAU platforms where appropriate.

• Design prototypes with explicit consideration for scalability, operability, data governance, and integration pathways if progressed beyond pilot.

AI Research & Innovation

• Research, evaluate, and stay current with emerging AI approaches, tools, and techniques.

• Assess the relevance and applicability of new AI capabilities within a regulated reinsurance environment.

• Apply insights from research to inform experimentation, solution design, and technical direction.

• Engineering Excellence & Governance Alignment

• Ensure adherence to PL Technology engineering standards, architecture principles, and security controls.

• Partner with Enterprise Architecture and Information Security to meet governance and regulatory expectations.

• Ensure solutions respect data principles including ownership, lineage, residency, and PII handling.

• Apply approved integration patterns when accessing platforms, data products, or external services.

Collaboration & Knowledge Sharing

• Work alongside the Global Experience Analysis (Global EA) engineering team, sharing cloud and AI engineering insights, patterns, and lessons learned.

• Act as a bridge between Business & AI experimentation and wider PL Re technology capabilities to accelerate AI adoption consistently across the organisation.

Architecture & Platform Alignment

• Contribute to solution architecture designs, including high‑level logical and cloud architectures, for AI use cases progressing beyond experimentation.

• Align AI solutions with Architecture Governance, PL Re target architectures, approved platforms, integration patterns, and data architecture principles.

• Identify architectural impacts, dependencies, and technical debt risks arising from AI experimentation.

Skills & Experience

Essential

• Demonstrable experience designing, building, and piloting AI‑enabled solutions, including rapid prototyping and experimentation in partnership with business stakeholders.

• Strong hands‑on development experience (Python, APIs, modern application architectures).

• Experience translating business problems into practical, secure, and scalable technical solutions aligned to enterprise standards.

• Ability to translate business problems into secure, scalable enterprise‑aligned solutions.

• Experience evaluating and applying AI technologies within regulated or risk‑aware environments.

• Strong engineering judgement, balancing innovation with operational readiness and control.
  

Skills & Experience – Desirable

• Experience with AI, data, and analytics platforms on AWS or Azure, including managed AI or foundation model services.

• Exposure to actuarial, underwriting, pricing, analytics, or adjacent insurance technology environments.

• Familiarity with DevOps / DevSecOps practices, including CI/CD pipelines in enterprise settings.