Senior Python Engineer

O projekcie: VirtusLab is a leading European software consulting and engineering company, home to over 350 EU-based professionals. Our mission is to craft clean code and practical solutions with precision and purpose. We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering our team. As part of the expanding VirtusLab Group, we offer a compelling environment for those seeking to make a substantial impact in the software industry within a forward-thinking organization. About the role We’re looking for an engineer to join a small, startup-like team building a tool for deploying and configuring local AI inference on local devices like NVIDIA Spark. You’ll work on managing the model lifecycle (start/stop) and connecting coding agents—such as Claude Code, Codex, and others—to locally hosted models. Your daily work will be on the backend: adapting LiteLLM configuration as agent APIs (Claude Code, Codex, etc.) evolve, managing the lifecycle of docker images that ship the agents, integrating with the Hugging Face API for model downloads, and getting the production essentials right—idempotent endpoints, retries with backoff, and straightforward migrations. The team already has an internal MVP and talks directly with potential users. We’re on the road to making it open source. If you want to shape a developer tool from the early stages, this is it. ProjectLocal Inference Platform for Python Engineers Project Scope A desktop application for deploying and configuring local AI inference on local devices (e.g., NVIDIA Spark / DGX Spark), managing model lifecycle (start/stop), and connecting coding agents like Claude Code and Codex to locally hosted models. Currently an internal MVP, on the road to open source. Tech Stack Python, Asyncio, FastAPI, LiteLLM, Pydantic, PostgreSQL, AWS, Agent layer: LLM proxy routing from agents to BE Challenges Building a desktop control plane for local AI agents that abstracts away the messy parts of running LLMs on heterogeneous hardware — model lifecycle, proxy routing, agent configuration, and OS-specific quirks (path separators, signing, packaging) — while keeping the system extensible enough to evolve toward open source. Visualising model statistics and system load on top of a backend that actually does the deployment. Team Small, startup-like team on the JetBrains side — no bureaucracy, direct contact with potential users, fast iteration. Code reviews as part of the process. A few perks of being with us - Building tech community- Flexible hybrid work model- Home office reimbursement- Language lessons- MyBenefit points- Private healthcare- Training Package- Virtusity / in-house trainingAnd a lot more! Wymagania: What we expect in general: - Strong Python skills with experience in backend frameworks such as FastAPI or Flask.- Solid understanding of concurrent and asynchronous programming.- Ability to rapidly prototype and iterate on emerging ideas.- Engineering pragmatism: cutting complexity while keeping the system extensible.- System-level thinking: understanding how the Docker lifecycle, LLM proxy routing, model management, and the agent layer fit together.- Hands-on experience using LLMs daily, with the judgment to know when they help and when they don’t.- Self-motivation and the ability to take full ownership of features end-to-end.- Fluency in English, with good communication skills for a remote-first team. Nice to have: - Experience using Docker for local deployment and service orchestration.- Experience setting up and maintaining an LLM proxy layer such as LiteLLM or OpenRouter.- Experience with vLLM or ollama.- Familiarity with the HuggingFace API and model distribution patterns.- Experience with local GPU inference setups (CUDA, NVIDIA hardware)- Background working with coding agents (Claude Code, Codex, Aider) and their configurations. Seems like lots of expectations, huh? Don’t worry! You don’t have to meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! Codzienne zadania: - Working in a cross-functional team - Shaping solutions for a hybrid cloud infrastructure (Hadoop, Apache Spark, Azure) - Automated CI/CD pipelines and IaaC solutions - Work either a Scrum or a Kanban approach - Code review