AI Engineer - GenAI & Cloud

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