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Greater London, England, United Kingdom
Hybrid
Flow Engineering is an AI-native requirements platform for modern engineering organizations, enabling hardware teams to collaborate with AI agents to design, validate, and evolve complex systems with speed and rigor.
Flow is seeking software engineers to build AI-powered capabilities that help teams author, review, and manage requirements more effectively. You will work on agentic systems engineer and agentic domain engineer workflows—bringing AI into the center of how teams reason about their systems.
This role sits at the intersection of AI, product, and full-stack engineering: you will take ideas from prototype all the way to stable, observable features in production.
Design and ship AI-powered features such as assisted requirement drafting, consistency checks, impact analysis, and intelligent suggestions for systems and domain engineers.
Build agentic workflows that help “agentic systems engineers” and “agentic domain engineers” explore designs, simulate changes, and validate requirements.
Evaluate and integrate language models and related tooling, optimizing for reliability, latency, cost, and debuggability in production.
Build and maintain the surrounding infrastructure: data pipelines, evaluation harnesses, prompt and model management, observability, and safety/guardrails.
Work across the stack—from backend integrations and APIs to simple UI hooks—to deliver complete AI features, not just model endpoints.
Partner with product and customers to identify high-value workflows, run experiments, and iterate quickly based on usage.
5+ years developing production software, including designing, testing, and operating services at scale in a cloud environment.
Hands-on experience with modern LLM providers and tooling (e.g., OpenAI, Anthropic, Hugging Face, vector stores, RAG patterns).
Familiarity with prompt design, retrieval-augmented systems, evaluation methods, and safety/guardrail approaches.
Ability to reason about tradeoffs between different models, architectures, and deployment patterns and make pragmatic decisions.
Comfortable working in a high-ownership, fast-paced environment where experiments and iteration are the norm.
TypeScript/Node.js and Python for AI and backend services.
Modern LLM APIs and orchestration libraries for building agentic workflows.
Postgres and other managed cloud services for data and state.
Speed over everything: prototype AI workflows quickly, then harden what works.
Own, downscope, ship, iterate: one clear owner per feature, from prototype to production.
Fundamentals done well: evaluation, observability, and safety are part of the first version, not an afterthought.
Competitive salary and meaningful equity.
Health, dental, and vision coverage.
Flexible time off and support for experimentation, learning, and staying current with the AI ecosystem.
