Senior, Full Stack Engineer

Description

Enterprise AI (EAI) builds AI products that pair real modelling work with user-facing applications and ships them to production. Full Stack Engineers work across data, models, platforms, APIs and interfaces within one workflow.

Digital workers handle much of the hands-on build, generating services, APIs, interface components and tests, running evaluations and monitoring production. Engineers set the architecture and standards that this work follows and are accountable for the quality of what ships.

EAI products and agents rely on a governed context layer that makes data, meaning, permissions and history retrievable when needed. Full Stack Engineers build the products and agents that use this layer and decide how that context reaches users.

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

You will lead a large AI product area across modelling workflows, platforms, APIs and interfaces, owning its architecture, quality and results.

You will define how the team works with digital workers, through API, integration and interface standards and the review patterns that their output must meet under EAI's operating model.

You will build agentic research systems for ongoing experimentation, evaluation and product iteration, and raise the engineering bar by mentoring junior and mid-level engineers.

Responsibilities

• Set technical direction for the product area and its modelling pipelines, including boundaries, evaluation strategy and clear trade-offs between performance, reliability and cost.
• Design agentic research systems: auto-research agents that run experiments and summarise findings, agents that monitor and improve production models, and agents that scaffold and iterate on features.
• Work with product, design and research to decide what gets built, pushing back on weak ideas and explaining trade-offs plainly.
• Define which work digital workers can build and change without sign-off, which evaluation gates that work must pass, and when a human must review it under EAI's operating model.
• Set safe, effective patterns for AI-assisted development with coding and research agents that the team adopts.
• Lead the design of API layers serving several consumers, defining integration contracts and non-functional requirements.
• Set or improve API design standards across the product area and advise others on backend architecture decisions.
• Lead the building of shared infrastructure and tooling that cuts integration cost across teams, and drive its adoption.
• Design production-scale AI orchestration: agent frameworks, multi-step workflows, retrieval, tool use, guardrails and integration patterns.
• Set the team's standards for AI integration, evaluation and responsible deployment, including how products and agents use the governed context layer with clear permissions and provenance. Resolve production AI failures as they occur.
• Lead frontend architecture for the product area, defining the UI patterns and component library the team uses.
• Drive AI interface design that delivers measurable adoption, with confidence signals, error states, provenance and human override paths, and guide others on UX engineering quality.
• 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.
• Set engineering standards written for both humans and digital workers, with measurable gains in test coverage, API contract quality and deployment reliability.
• Mentor junior and mid-level engineers through pairing, review and direct feedback, aiming for visible growth in their delivery ability.

Requirements

• Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
• 8+ years combined across applied ML or data science and full-stack engineering, including significant time leading delivery of complex, user-facing AI products.
• Working knowledge across the AI product stack: modelling and evaluation, modern frontend frameworks, backend services and cloud deployment, such as React or Next.js, Python or TypeScript, and AWS, Azure or GCP.
• Experience leading API layer design for multiple consumers, with integration contracts and non-functional requirements that others build against.
• Experience designing production-scale AI orchestration, covering LLM integration, retrieval, tool and function calling, agent frameworks, evaluation, and trade-offs between cost, latency and quality.
• Experience leading frontend architecture and LLMOps for a product area, including evaluation pipelines, production monitoring and model lifecycle management.
• Experience designing and running agentic systems that drive continuous research or product iteration, plus AI-assisted development patterns that a team has adopted.
• Preferred: Master's or PhD in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
• Preferred: Technical lead on one or more AI products combining modelling and full-stack delivery, from first build through to scale.
• Preferred: Built or improved internal developer or research tooling, particularly for AI-assisted development and auto-research.
• Preferred: Experience in a regulated industry, such as life sciences, where data integrity and auditability are mandatory.
• Preferred: Has led API layer design for multiple consumers, set integration contracts and non-functional requirements for the team, and set or improved API design standards across a product area.
• Preferred: Has designed production AI orchestration, including agent frameworks, multi-step workflows and use of the governed context layer, and set team standards for AI integration, evaluation and responsible deployment.
• Preferred: Has led frontend architecture for a product area, including the UI patterns and component library.
• Preferred: Has driven AI interface design with measurable adoption and guided others on UX engineering quality.
• Preferred: Has led LLMOps and observability strategy, designed evaluation pipelines and production monitoring at scale, and set standards for model lifecycle management, versioning and inference cost tracking.
• Preferred: Has set engineering standards that include patterns for agent-generated work, and driven measurable gains in test coverage, API contract quality or deployment reliability.
• Preferred: Has designed agentic research systems in production, such as auto-research, evaluation-driven improvement loops and agent-driven prototyping.
• Within 12 months: lead delivery of a major AI product area spanning modelling and application work, owning architecture and results.
• Within 12 months: define the team's patterns for agent-generated work and its standards for APIs, AI integration and interfaces, with measurable quality or reliability gains.
• Within 12 months: design and ship an agentic research system that measurably speeds up experimentation or product iteration.
• Within 12 months: mentor junior and mid-level engineers until they deliver with more skill and independence.

Conditions

This role is based in the United States.

Flexible working is available, including hybrid arrangements depending on team needs.

Equal opportunity applies; applicants are considered without regard to protected characteristics.

Benefits

• Details of the US benefits programme are available on request.