Applied AI Engineer – (Europe or UK)

Applied AI Engineer – (Remote) (Europe or UK - Required) 100k – 150k Euro Responsibilities - Design and build the core primitives behind our agentic systems, including memory, context management, tool-calling, orchestration, and feedback loops - Move us from one-off AI solutions toward reusable infrastructure where new workflows can be introduced quickly and with predictable reliability - Build the evaluation framework that allows us to understand how our agents perform and why they succeed or fail - Make testing, tracing, debugging, and evaluating AI systems as fundamental to our engineering process as unit testing traditional software - Develop the systems that allow us to confidently assess an agentic workflow before deploying it into production - Push forward how we use LLMs to build software itself - Create workflows where coding agents and specialized subagents can explore repositories, implement changes, review architecture, check conventions, run evaluations, and iterate with minimal human coordination - Use agentic engineering workflows extensively in your own day-to-day development - Design systems where specialized agents, tools, deterministic software, and humans work together effectively - Understand when a problem should be solved with an LLM, when traditional software is the better solution, and when multiple coordinated agents can materially improve the outcome - Act as the bridge between complex operational workflows and engineering - Work closely with our operational and product teams to understand how work actually happens across acquired agencies, identify the highest-leverage opportunities for automation, and turn them into reliable production systems - Help define the architectural patterns we use for agentic systems as the company scales - You won't be joining as engineer #20 on an established AI platform. You will be an early core member of the team with significant influence over our technical approach, tooling, and engineering standards Requirements - Strong software engineering background with experience independently delivering complex systems from idea through to production - Hands-on experience building **AI or agentic systems that have operated in production** - Experience dealing with real-world AI challenges such as reliability, latency, context management, failure modes, evaluation, and observability - Strong understanding of **agents, tool use, structured outputs, context management, orchestration, evaluation, and feedback loops** - Advanced practical use of modern LLMs — not simply using ChatGPT or Claude occasionally, but understanding how to build reliable workflows around probabilistic systems - Experience using **coding agents and agentic development workflows** as part of your own engineering process - Experience decomposing complex engineering tasks across agents or specialized subagents and designing mechanisms for them to verify and improve their outputs - Strong architectural judgment and ability to decide when to use LLMs, deterministic software, human intervention, or a combination of approaches - Experience with **TypeScript / Node.js**, or strong experience building AI systems in Python with the ability and willingness to apply that knowledge in a TypeScript-first environment - High autonomy, ownership, and comfort making decisions under ambiguity - Strong product judgment and ability to connect technical decisions to operational and business outcomes - Strong communication skills and fluency in English - Startup mentality: resilience, adaptability, and ability to thrive in a fast-paced environment Originally posted on Himalayas