Principal Software Engineer/Principal AI Engineer

About the team:

Embedded Innovation Teams are cross-functional squads embedded within our segments to rapidly turn internal AI experimentation into validated, reusable solutions, building the capabilities we need to deliver customer value and growth. We work problem-first rather than tool-first, directly inside segment and function teams, improving the internal workflows that help our people deliver better outcomes for customers, faster.

About the role:

As a Principal AI Engineer, you will be the technical lead for an embedded Innovation Squad, owning technical execution from discovery through to production. You will assess and harden existing AI prototypes, deliver new ones, and establish the agentic AI patterns, orchestration, tool use, memory, and retrieval that other squads reuse. You are the senior source of expertise on AI architecture, integration, and delivery.

You will work directly with internal stakeholders and end users in the host function, finalizing requirements, making the architectural calls, and codifying validated solutions into reusable patterns, reference implementations and starter kits. You may also provide sizing, infrastructure, and budget recommendations, and you will mentor and elevate the engineers around you.

Key Responsibilities:

• Owning technical execution from discovery to production; build, assess, and harden prototypes and proofs of concept, and deliver new ones.
• Designing and governing reusable agentic AI patterns (orchestration, tool use, memory, retrieval).
• Ensuring delivery meets architecture, security and data-governance standards; recommend infrastructure, hosting and data residency.
• Codifying validated solutions into patterns, reference implementations, and starter kits so they can be picked up and scaled across other functions.
• Providing design input, finalizing requirements, and work directly with internal stakeholders and end users in the host function.
• Writing specifications, fix complex bugs, and design complex data models.
• Acting as the primary technical contact for external resources and the squad's go-to on coding and AI.
• Establishing baselines and report measurable outcomes for every initiative.
• Maintaining visibility of in-flight AI pilots within the function to prevent duplication and surface candidates for reuse.
• Ensure alignment with enterprise non-negotiables: security, compliance and central technology infrastructure standards.
• Continuously evaluate emerging technologies and mentor and elevate team members.

Requirements:

• Engineering experience: 10+ years in software engineering, with hands-on AI system design, build, or integration, and a track record of taking AI prototypes to production in enterprise environments.
• Agentic AI / LLMs: LLM orchestration, prompt engineering, context-window management and production guardrails; MCP, tool-augmented agents and RAG at enterprise scale.
• Enterprise integration: Integrating AI into enterprise systems via APIs and data pipelines, with strong security, data governance and residency practices.
• Cloud: AWS, Azure, or GCP, including managed AI/ML services.
• Delivery: Agile, CI/CD, modern SDLC, and TDD.
• Data and architecture: Modelling across relational, columnar and vector stores, grounded in sound architectural principles.
• Ways of working: Thrives in ambiguity, framing the problem, extracting defined spec, and prioritises shipping validated outcomes over perfect solutions, moving pilots to production at pace.
• Languages: Python, Java, TypeScript/JavaScript, SQL, and relevant AI SDKs.
• Leadership and delivery management: Plans and sequences work, manages scope, dependencies and priorities, removes blockers, and drives delivery to keep innovation moving.
• Communication and influence: Presents concisely to senior management, host-function stakeholders, and cross-functional teams, and builds buy-in and drives technical decisions across.
• Commercial awareness: Budget-aware; partners with internal and external resources, including managed services and staff augmentation.
• Measurement: Establishes baselines and instruments solutions to track productivity, cost, and quality outcomes from pilot through scale.