GenAI Developer

What youll 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. Youre 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 618 months youre in a new engagement. New industry new architecture decisions new stakeholders to earn trust from. Youll face more distinct technical challenges in two years here than most engineers see in five.

Work thats 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 arent solved yet and youll 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. Thats what keeps Redeploy consistently ahead and its what youll feel from day one.

The perks. 30 days vacation hybrid work and flexible hours private medical insurance pension (ITP1) wellness allowance 5000 SEK free choice of tools and tech free breakfast soda and snacks yearly gatherings and AWs 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 dont consider a use-case done until its working end-to-end interface included.

You might come from a fullstack engineering or traditional ML/Data background. What matters is that youre already building AI solutions whether thats 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 wholl 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.