Senior, Full Stack Engineer

Enterprise AI (EAI) builds AI products that combine real modelling work with user-facing applications, and ships them to production. Full Stack Engineers move across data, models, platforms, APIs and interfaces in a single workflow. In our operating model, digital workers do much of the hands-on build. They generate services, APIs, interface components and tests, run evaluations and monitor what is in production. Our engineers set the architecture and standards that work is built to, and own the quality of what ships.

EAI products and agents draw on a governed context layer that makes data, meaning, permissions and history retrievable at the moment they are needed. Full Stack Engineers build the products and agents that consume it, and decide how that context reaches users.

The Senior Full Stack Engineer leads the design and delivery of a significant AI product area, spanning modelling workflows and user-facing applications. The role sets the team's standards for APIs, AI integration, interfaces and observability, designs the agentic research systems that drive continuous iteration, and mentors junior and mid-level engineers.

• Leads a significant AI product area across modelling workflows, platforms and APIs and user-facing interfaces, owning architecture, quality and outcomes.

• Defines how the team builds with digital workers through the API, integration and interface standards and the review patterns their work must meet within EAI's operating model.

• Designs agentic research systems that drive continuous experimentation, evaluation and product iteration, and raises the engineering bar by mentoring junior and mid-level engineers.

Key Responsibilities

Technical Leadership & Research Practice

• Set technical direction for the product area and its modelling pipelines, defining boundaries, evaluation strategies and defensible trade-offs on performance, reliability and cost.

• Design agentic research systems: auto-research agents that run experiments and synthesise findings, agents that monitor and improve production models, and agents that scaffold and iterate on features.

• Partner with product, design and research to shape what gets built, challenging weak ideas and communicating trade-offs clearly.

Agent-Operated Engineering

• Define the team's patterns for agent-generated work within EAI's operating model: what digital workers build and change on their own, which evaluation gates their work must pass, and when it needs human review.

• Set the team's AI-assisted development patterns for coding and research agents, establishing safe, effective use the team adopts.

Platforms & APIs

• Lead the design of API layers that serve multiple consumers, defining integration contracts and non-functional requirements for the team.

• Set or improve API design standards used across the product area, and guide others on backend architecture decisions.

• Lead the creation of shared infrastructure and tooling that materially reduces integration cost across multiple teams, and drive its adoption.

AI Integration & Context

• Design AI orchestration architectures at production scale: agent frameworks, multi-step workflows, retrieval, tool use, guardrails and integration patterns.

• Define the team's standards for AI integration, evaluation and responsible deployment, including how the product area's products and agents draw on the governed context layer with clear permissions and provenance. Resolve production AI failure modes as they arise.

AI-Native Experience

• Lead frontend architecture for the product area, defining the UI patterns and component library the team works from.

• Drive AI interface design that achieves measurable adoption, with confidence signals, error states, provenance and human-override paths, and guide others on UX engineering quality.

LLMOps & System Health

• Lead the team's LLMOps and observability strategy, designing evaluation pipelines and production monitoring at scale.

• Define standards for AI model lifecycle management, versioning and inference cost tracking across the product area.

Standards & Mentorship

• Set engineering standards for the team, written to be followed by humans and digital workers alike, with measurable improvements in test coverage, API contract quality and deployment reliability.

• Mentor junior and mid-level engineers through pairing, review and direct feedback, with visible improvement in their delivery capability.

Why You?
Basic Qualification
We are seeking professionals with the following required skills and qualifications to help us achieve our goals:

• Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience
• 8+ years of combined experience across applied ML or data science and full-stack engineering, with significant time leading delivery of complex, user-facing AI products.

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