Software Engineer- London
Job description
Flow Engineer at Flowengineering.
About the role
This role creates AI-powered tools that enable requirement authoring, review, and management. The team delivers agentic workflows that position systems and domain engineers at the center of system reasoning. You advance prototypes into stable, observable production features.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
What you'll do
Designs are explored, changes are simulated, and requirements are validated through agentic workflows built for systems and domain engineers.
Complete AI features are delivered by combining backend integrations and APIs, including simple UI hooks, instead of offering only model endpoints.
High-value workflows are identified, experiments are run, and iteration is accelerated based on usage through partnerships with product and customers.
Requirements
Production software is developed with 3+ years of experience, including designing, testing, and operating services at scale in a cloud environment. Designing, testing, and operating services at scale in a cloud environment is required alongside 3+ years of production software development.
Hands-on experience with modern LLM providers and tooling such as OpenAI, Anthropic, Hugging Face, vector stores, and RAG patterns is required. Experience with OpenAI, Anthropic, Hugging Face, vector stores, and RAG patterns is required as hands-on familiarity with modern LLM providers and tooling.
Prompt design, retrieval-augmented systems, evaluation methods, and safety/guardrail approaches are familiar. Familiarity with prompt design, retrieval-augmented systems, evaluation methods, and safety/guardrail approaches is expected.
Tradeoffs between different models, architectures, and deployment patterns are reasoned about to make pragmatic decisions. Pragmatic decisions are made by reasoning about tradeoffs between models, architectures, and deployment patterns.
High-ownership, fast-paced environments where experiments and iteration are the norm are comfortable. Comfort arises from working in high-ownership, fast-paced environments where experiments and iteration are standard.
Practical notes
This role is based in London. Health, dental, and vision coverage are provided. Competitive salary and meaningful equity are offered. Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
The team focuses on speed, ownership, and delivering fundamentals such as evaluation, observability, and safety early. The stack is AI-leaning, using TypeScript, Python, LLM APIs, and managed cloud services.
Questions to ask
Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.