TL;DR
→ You design and build production-grade RAG and agentic AI systems end-to-end. From use-case definition through to a working interface in users' hands.
→ Consulting across the Nordics, with clients spanning industries from financial services to manufacturing.
→ You work in cross-functional delivery teams alongside Data Engineers and Platform Engineers, each owning a distinct layer of the stack.
→ Certifications without a debate. Colleagues who are obsessed with the craft. Freedom to grow your way.
What you'll do
As a GenAI Developer, you own the AI application layer end-to-end: the systems that ingest, retrieve, reason, and respond, plus the lightweight interfaces that put them in front of users. You're the person closest to the end customer. You turn a business problem into a working use-case, from gathering requirements and aligning stakeholders to shipping a production system people actually use.
You come in before the solution is defined. Together with the client, you shape what gets built, then you build it. You work alongside Data Engineers and Platform Engineers, each owning a distinct layer of the stack. Your layer is where the model meets the business problem, and where the user meets the product.
A few examples of what this looks like in practice:
- A production RAG system for a financial services client, letting internal teams navigate regulatory documentation using generative AI. From ingestion through to answer generation, quality evaluation, and a lightweight front-end for day-to-day use.
- An agentic AI solution in the FinOps space, applying LLM reasoning over infrastructure data to drive cloud cost optimization beyond what rule-based tooling can handle.
- AI capability building at enterprise clients: use-case workshops, requirements definition, and architecture advisory on agents and coding tools for senior technical audiences.
The bar for what "done" means here is high: production-grade, observable, secure and used.
What you get
Assignments that accelerate you. Every 6–18 months you're in a new engagement. New industry, new architecture decisions, new stakeholders to earn trust from. You'll face more distinct technical challenges in two years here than most engineers see in five.
Work that's hard to come by elsewhere. Agentic AI in enterprise environments is one of the most technically demanding spaces in the industry right now. The problems here aren't solved yet, and you'll be among the people solving them.
Your direction, your call. Want to go deep technically and become the go-to person for enterprise AI architecture? Go for it. Want to lead client engagements, own stakeholder relationships and drive use-cases from discovery to production? Also go for it. Both paths are real and equally valued.
People who make you better. The people here are genuinely passionate about technology, not as a job, but as something they care about. Engineers who go deep because they want to, follow the space obsessively, and get restless when things stop moving. That's what keeps Redeploy consistently ahead, and it's what you'll feel from day one.
The perks. 30 days vacation · hybrid work and flexible hours · private medical insurance · pension (ITP1) · wellness allowance 5,000 SEK · free choice of tools and tech · free breakfast, soda and snacks · yearly gatherings and AW's · a team with genuine interests outside work — gaming, food, running, padel, golf, football, cycling.
Who you are
You care whether your AI systems actually work, not just whether they run, and you have an instinct for finding failure modes before they reach production. You care about how users interact with it, and you don't consider a use-case done until it's working end-to-end, interface included.
You might come from a fullstack engineering or traditional ML/Data background. What matters is that you're already building AI solutions, whether that's a side project, a POC, or a deep dive into a new framework. Curiosity is a given. The question is whether you act on it.
You like being close to the people who'll use what you build. You can run a requirements workshop, figure out what a stakeholder actually needs versus what they asked for, and explain why a retrieval pipeline is underperforming, all with the same clarity.
What you bring
- Strong requirements engineering and stakeholder management skills, you can drive a use-case from problem definition to delivery, keeping technical and non-technical audiences aligned throughout
- Strong Python skills and solid software engineering fundamentals: clean code, testing, version control, system design
- Production experience with RAG architectures: chunking strategies, embedding models, vector search, hybrid retrieval, reranking, and retrieval evaluation
- Hands-on experience with agentic frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI, including tool use, memory management, and multi-agent orchestration
- Experience with Azure AI Services or Azure OpenAI, including integration across Azure services in production environments
- Ability to build lightweight front-end interfaces for AI applications to make prototypes and internal tools usable beyond the terminal
Strong plus: multi-agent architecture design, front-end development skills (React, Streamlit, or similar), containerization and MLOps awareness, experience in regulated industries, Azure AI-102 certification, prior consulting experience, Swedish language skills.
About Redeploy
Redeploy is where cloud, data, and AI come together in production. We help Nordic enterprises design, build, and operate modern tech platforms and AI solutions that are secure, scalable, and production-ready. Engineers at heart, we work hands-on across Azure, AWS, and Databricks from strategy to operations.