Forward Deployed AI Engineer

What you'll do Get under the hood of each client's architecture, data and integrations. Turn a scoped brief into an agentic system running in the client's own environment, end to end. Lead the engineers on each project and set the technical direction. Be the client's day-to-day technical contact: updates, questions, and the things that were clear on paper but not in practice. Keep several projects moving at once, and surface scope changes instead of agreeing to them on the spot. Must-have 5+ years in software engineering, with a background in computer science, machine learning and generative AI. A track record of shipping LLM, RAG and agentic systems to production and keeping them running, with the depth to work out why a model behaves the way it does. Hands-on with RAG pipelines (vector databases, streaming, multi-tenancy), multiple LLM providers, and frameworks like LangChain, vLLM and PyTorch or TensorFlow. Strong Python, solid engineering habits (version control, testing, code review) and containerized deployments on AWS, GCP or Azure. Quick to pick up an unfamiliar codebase and build inside someone else's stack. A solution-based, consultative approach. You start from the client's problem, not from the tool or product you'd like to sell them. A strong communicator in professional English. You can lead a client conversation and explain technical decisions to people who'll never read the code. Self-directed. You run with a brief without needing someone to check in on you. Nice to have Things you've built on your own time, such as side projects, open source or agent experiments, and the story of why you built them. Real experience with LangGraph, CrewAI, AutoGen or the OpenAI Agents API. Time at a startup or scale-up where priorities shift quickly. Why you'll like it You'll see a wide range of companies and problems in a short time, so the work never gets repetitive. There's no fixed playbook, so the ownership is real and the systems you build are yours to get right. You'll work directly with clients, with strong backing from leadership to move fast. Practical details Location: Stockholm preferred Travel: occasional client site visits Interview process: Talent Acquisition call → hiring manager interview → practical AI engineering assessment → final interview