Applied AI Engineer

You must haveMin 2.5 years building software, with solid Python.Some hands-on experience building with LLMs or AI — at work, in a side project, a hackathon, or open source. We care more about what you've built than about years on paper.Comfort working with people, not just code — happy to talk to clients, ask good questions, and explain what you're doing.Curiosity and drive — you like figuring things out and don't wait to be told every step.Good English (B2+) — enough to hold a working conversation with a client.Nice to haveA project you can show us — a repo, a demo, or something you built with an LLM.Experience with agent frameworks (LangGraph, CrewAI, Pydantic AI) or MCP.Familiarity with cloud (GCP / AWS / Azure) or Docker.RAG, evaluation, or prompt/context-engineering experience.Anything that shows you ship: competition results, open-source contributions, internal tools other people use. About deepsense.aideepsense.ai is a 120-person AI/ML consultancy with Anthropic and OpenAI partner credentials. For over a decade we've delivered applied AI projects for companies like J&J, Sky, John Deere, and GLS — spanning LLM applications, agents, MLOps, and data science. We're a people-first organization that believes in deep technical craft and real ownership. Our engineers don't just advise; they build things that run in production.About the roleYou'll spend most of your time building — designing and shipping LLM-powered applications, agents, and the pipelines around them. You'll work close to clients, usually alongside a senior engineer or delivery manager, you get real client exposure. Expect to move between writing code, trying things out, and explaining what you built to the people who'll rely on it.It's a great seat to grow in: you'll see how real AI engagements are scoped and delivered, pick up the client-facing craft from people who are good at it, and take on more ownership as fast as you earn it. ,[Build LLM-powered applications and agents — RAG pipelines, integrations, evaluations — and get them running in production., Turn a rough idea or a client need into a working proof of concept, fast., Work directly with clients alongside senior colleagues — join discovery and workshops, demo what you've built, and learn to explain your work to non-engineers., Write clean, dependable code, and care about whether what you ship actually works for the people using it., Keep up with the fast-moving AI stack — new models, tools, and patterns — and share what you find with the team., Contribute to shared assets: reusable components, demos, and internal tools others can build on.] Requirements: Python, LLMs, LangGraph, CrewAI, MCP server , Clouds, Docker Additionally: Sport subscription, Training budget, Private healthcare, Lunch card, Small teams, International projects, Free coffee, Canteen, Free snacks, Free beverages, Free lunch, In-house trainings, Modern office, No dress code, Free breakfast.