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