ai engineer

Sii Poland provides IT services and consulting, including production-grade generative and classical AI solutions across cloud platforms such as Azure, AWS, and GCP.

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

• At least 4 years of experience in software or ML engineering with hands-on experience shipping AI/ML systems to production;
• Strong Python skills for maintainable, tested production services;
• Practical experience with AI/ML frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent;
• Working knowledge of 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, including model versioning, experiment tracking, CI/CD for ML, and monitoring;
• Ability to work autonomously while collaborating with architects, data engineers, and product teams;
• Fluent English, both written and spoken;
• Fluent Polish;
• Residing in Poland;
• Nice to have: Experience fine-tuning LLMs with LoRA or QLoRA, classical ML with scikit-learn or XGBoost, time series forecasting, recommendation systems, containerized deployments with Docker or Kubernetes, infrastructure-as-code, contributions to open-source AI/ML projects, published technical writing.

• AI Grant with dedicated budget, resources, and two paid weeks for an AI project;
• Access to 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 and well-equipped offices;
• Internal training centre and access to many experts.