Senior AI Engineer

We are building a next-generation AI-powered Enterprise Architecture (EA) platform, and we're looking for a Senior AI Engineer to help define its intelligence layer. This is a unique opportunity to work at the intersection of cutting-edge AI and the complex, high-value domain of enterprise architecture-used daily by architects, CIOs, and business analysts at organisations worldwide. You'll work closely with our engineering team, product managers, and stakeholders to design and build LLM-powered assistants, intelligent reasoning over enterprise data, and agentic workflows integrated with real enterprise systems. This role combines deep AI architecture expertise, backend engineering, and system integration. You won't just be building features-you'll be shaping the technical direction of AI across the entire platform., * AI Architecture & System Design

• Design end-to-end LLM-based systems (RAG, agents, memory, tool use)
• Define scalable and reusable AI architecture patterns (prompting, orchestration, evaluation)
• Build context-aware AI leveraging structured and unstructured data

• Data Modeling & Context Engineering

• Design rich data abstractions and context models for enterprise knowledge
• Enable AI reasoning over complex enterprise relationships and hierarchies
• Design retrieval and reasoning approaches that go beyond vector search, incorporating structure, relationships, and domain semantics

• Backend & Distributed Systems

• Build scalable AI services and APIs
• Design pipelines for real-time and async workflows
• Ensure performance, reliability, observability

• Integration & AI Ecosystem

• Integrate LLM providers, vector DBs, enterprise systems
• Build tooling layer for agent interaction

• Design and implement agentic workflows (planning, tool use, multi-step reasoning) integrated with enterprise systems

• Evaluation & Production Readiness

• Define and implement evaluation approaches for LLM systems (quality, grounding, task success)
• Implement guardrails, monitoring, and logging
• Optimize latency, cost, and system reliability

• Collaboration

• Work closely with product, engineering, and domain experts to drive delivery of AI capabilities and influence roadmap direction

• Own technical design and implementation of key components, contributing to broader system decisions

• Proactively share knowledge, raise the bar on engineering quality, and improve team practices, * Define the AI technical direction of a globally-used Enterprise Architecture platform

• 6+ years in software / ML engineering

• Strong Python and hands-on experience building production-grade LLM-based systems (RAG, agents, evaluation)

• Experience designing and building multi-step or stateful AI systems (agents, workflows, or pipelines)

• Experience with APIs, microservices, cloud platforms (AWS, Azure, or GCP)

• Experience defining and applying evaluation approaches for LLM systems (metrics, benchmarking, or testing strategies)

• Familiarity with containerization and orchestration (e.g., Docker, Kubernetes)

• Strong system design and architecture skills

Preferred

• Experience with graph databases or structured data modeling (e.g., knowledge graphs, complex schemas)

• Experience designing systems where AI interacts with structured domain models or enterprise data

• Experience with enterprise systems or metadata platforms (e.g., EA tools, CMDB, similar)

• Experience building agentic orchestration layers (tool use, planning, multi-step reasoning)

You Might Be a Fit If You…

• Have experience bridging AI research and production engineering-and thrive in that space
• Are energised by technically complex, ambiguous problems where the right answer isn't obvious yet
• Can speak both "LLM internals" and "enterprise architecture" fluently, or are excited to learn the latter
• Want to own meaningful technical decisions on a globally-used SaaS platform, not just implement tickets
• Take pride in building AI systems that are not just impressive in demos, but reliable and valuable in production

• Work alongside a world-class engineering team and visionary product leadership
• A culture that genuinely values experimentation, continuous learning, and measured impact
• Competitive compensation, hybrid working from London, and real ownership of a critical capability