AI Engineer – GenAI & Cloud (f/m/x)

We need engineers who build AI systems that work outside of a Jupyter notebook. You’ll take architectures designed for real problems and turn them into production services — reliable, observable, and cost-efficient. Your day-to-day will involve writing Python, wiring up cloud infrastructure, and solving the unglamorous problems that make AI actually useful: data quality, latency, evaluation, and deployment.

You’ll work across the generative and classical AI stack — building knowledge-grounded AI systems, integrating LLMs into applications, training and deploying traditional ML models, and keeping it all running in production on Azure, AWS, or GCP.

What we offer:

AI Grant — Stop talking about AI and start building it. Our AI Grant gives you dedicated budget and resources to turn your wildest AI idea into a working project, backed by two paid weeks to focus on nothing else.

AI Center of Excellence — Work alongside specialists in agentic AI, sovereign AI, generative and discriminative AI. This isn’t a siloed team — it’s the people you’ll learn from and build with daily.

Your tools, your choice — Full access to AI-powered development tools including Claude, Cursor, and GitHub Copilot. Pick what works best for you.

Real project variety — From generative AI for legal document compliance, through agentic systems in manufacturing environments, to enterprise-scale AI platforms, computer vision, and autonomous driving. You won’t get bored.

Conference and speaking support — Want to attend conferences? We’ll back you. Want to speak at them? Even better — we’ll support you with dedicated preparation time and bonuses.

Your tasks

• Build and deploy knowledge-grounded AI systems end-to-end: data ingestion, chunking, embedding pipelines, retrieval logic, re-ranking, and response generation

• Develop agentic applications — tool integrations, planning loops, memory management, guardrails — using frameworks like LangGraph, LangChain, Semantic Kernel, or equivalent

• Implement and maintain ML pipelines for classical use cases: prediction, classification, recommendation, and optimization models

• Deploy and optimize model serving infrastructure: API endpoints, batching, caching, GPU utilization, and cost management across cloud environments

• Write clean, tested, production-grade Python — not prototype code that someone else has to rewrite

• Build evaluation and monitoring pipelines: automated quality checks, drift detection, latency tracking, and human-in-the-loop feedback loops

• Work with cloud-native AI services on at least one major platform (Azure, AWS, or GCP) to implement scalable solutions

• Collaborate with AI Architects on technical design and with data engineers on data availability and quality

Requirements

• At least 4 years in software or ML engineering, with hands-on experience shipping AI/ML systems to production

• Strong Python skills — not just scripting, but writing maintainable, tested code for production services

• Practical experience with AI/ML frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent

• Working knowledge of at least one major cloud platform (Azure, AWS, or GCP) and its AI/ML services

• Experience with vector databases, embedding models, and retrieval systems in real-world applications

• Familiarity with MLOps fundamentals: model versioning, experiment tracking, CI/CD for ML, and monitoring

• Ability to work autonomously while collaborating effectively with architects, data engineers, and product teams

• Fluent English (both written and spoken)

• Fluent Polish required

• Residing in Poland required

Nice-to-have requirements

• Experience fine-tuning LLMs (LoRA, QLoRA) or working with model training pipelines

• Background in classical ML — scikit-learn, XGBoost, time series forecasting, or recommendation systems

• Experience with containerized deployments (Docker, Kubernetes) and infrastructure-as-code

• Contributions to open-source AI/ML projects or published technical writing