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