GenAI Developer

→ 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.

→ You work in cross-functional delivery teams alongside Data Engineers and Platform Engineers, each owning a distinct layer of the

stack.

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 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.

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.

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.

• 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

• 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

• 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.

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.