Description
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.
Responsibilities
•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
Requirements
•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
Nice to have
•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
Conditions
•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
