ai engineer for production AI systems

The team builds production AI systems, including knowledge-grounded AI applications, generative and classical AI models, and cloud-based services. Its projects span legal document compliance, manufacturing, enterprise AI platforms, computer vision, and autonomous driving.

• Build and deploy end-to-end knowledge-grounded AI systems, including data ingestion, chunking, embedding pipelines, retrieval, re-ranking, and response generation
• Develop agentic applications with tool integrations, planning loops, memory management, and guardrails
• Implement and maintain ML pipelines for prediction, classification, recommendation, and optimization
• Deploy and optimize model-serving infrastructure, including API endpoints, batching, caching, GPU utilization, and cost management across cloud environments
• Write clean, tested, production-grade Python
• Build evaluation and monitoring pipelines for quality checks, drift detection, latency tracking, and human-in-the-loop feedback
• Implement scalable solutions using cloud-native AI services on Azure, AWS, or GCP
• Collaborate with AI Architects on technical design and with data engineers on data availability and quality

• At least 4 years of software or ML engineering experience, including hands-on experience shipping AI/ML systems to production
• Strong Python skills and experience 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 and its AI/ML services: Azure, AWS, or GCP
• Experience with vector databases, embedding models, and retrieval systems in real-world applications
• Familiarity with MLOps fundamentals, including model versioning, experiment tracking, CI/CD for ML, and monitoring
• Ability to work autonomously and collaborate effectively with architects, data engineers, and product teams
• Fluent English, written and spoken
• Fluent Polish
• Residing in Poland
• Будет плюсом: LLM fine-tuning (LoRA, QLoRA) or model training pipelines, classical ML with scikit-learn or XGBoost, time series forecasting or recommendation systems, containerized deployments with Docker or Kubernetes, infrastructure-as-code, open-source AI/ML contributions, published technical writing

• An AI Grant provides dedicated budget and resources for personal AI projects, plus two paid weeks to focus on a project
• Access to an AI Center of Excellence and AI-powered development tools, including Claude, Cursor, and GitHub Copilot
• Support for conference attendance and speaking, including dedicated preparation time and bonuses
• Profit sharing
• Passion Sponsorship program
• Regular integration events and trips
• Medical care
• Comfortable, well-equipped offices