Staff, 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 Staff Full Stack Engineer sets technical direction for the AI product stack across EAI. The role is accountable for the integration and orchestration architecture through which EAI products and agents draw on the governed context layer, and is EAI's internal authority on how AI capabilities reach users.

• Sets multi-quarter technical direction for the AI product stack across EAI: modelling workflows, platforms and APIs, AI-powered interfaces and the agentic systems that connect them.

• Owns the integration and orchestration architecture through which EAI products and agents draw on the governed context layer, and defines the standards engineers and digital workers build to.

• Defines EAI's agentic research and development practice for continuous experimentation, auto-research and agent-driven application development, and develops the senior engineers who carry it forward.

Key Responsibilities:

Technical Strategy & Research Practice

• Set multi-quarter technical direction for the AI product stack across EAI, and lead the most ambitious end-to-end bets, turning vague, high-stakes problems into shipped systems with durable impact.

• Define EAI's agentic research practice for continuous experimentation and auto-research, evolving production models, and using agents to drive research and application iteration.

Agent-Operated Engineering

• Set the operating model for agent-generated engineering work: what digital workers build and change on their own, what requires human approval, which evaluation gates their work must pass and how incidents escalate.

• Set the AI-assisted development standard: the tooling, workflows and practices for coding and research agents, with measurable impact on velocity and quality.

Platforms & APIs

• Define API and platform architecture standards adopted across EAI engineering teams, covering contracts, authentication, versioning and non-functional requirements.

• Drive architectural coherence across modelling pipelines, evaluations, retrieval, agent orchestration, application services, frontends, data and observability, without becoming a bottleneck. Shape platform decisions that measurably improve reliability and developer productivity.

AI Integration & Context

• Shape EAI's AI integration architecture: LLM selection, orchestration patterns, retrieval and tool use, and LLMOps standards.

• Own how EAI products and agents draw on the governed context layer over standard protocols such as MCP, with consistent patterns for retrieval, permissions and provenance.

AI-Native Experience

• Define EAI's frontend engineering standards and shape how AI capabilities reach users across the portfolio.

• Lead EAI's approach to AI-native UX: designing for non-deterministic, agent-driven interfaces, with confidence signals, error states, provenance and human-override paths that earn user trust.

LLMOps & System Health

• Define EAI's LLMOps standards for observability, evaluation and governance of production AI systems, and lead enterprise-scale evaluation and monitoring infrastructure.

• Own long-term system health across reliability, performance, cost, security, model behaviour, safety and abuse, as EAI's authority on production AI system health.

Standards & Technical Leadership

• Define full-stack engineering standards adopted across EAI pods, written to be followed by humans and digital workers alike.

• Shape EAI's developer tooling and shared component strategy, leading foundational investments such as evaluation frameworks, agent runtimes, experiment management, internal SDKs and shared components that reduce duplication and change how teams build.

• Develop senior engineers towards greater architectural independence, and partner with research, product, design, data engineering and platform teams on the hardest product problems.

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.
• 10+ years of experience in applied machine learning (ML) or data science and full-stack engineering, including experience building and operating user-facing AI products.

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