GenAI and LLM Operations Engineer

What we offer

• Flexible hybrid working model - modern office in Warsaw
• Career development programs
• Access to trainings, certifications, and conferences
• Competitive bonus structure
• Private medical care, Life and travel insurance, plus pension plan
• Sports card co-financing
• Meal subsidies in the office
• Additional days off
• Budget for home office setup
• Relocation support if needed

Requirements

• Hands-on experience with Generative AI and LLM-based applications (e.g., LangChain or similar frameworks)
• Strong knowledge of LLM operations, deployment, and scaling in production
• Experience with fine-tuning large language models for domain-specific use cases
• Familiarity with experiment tracking tools (e.g., MLflow) is a plus
• Proficiency in Python and building data pipelines or AI solutions
• Experience working with cloud platforms (AWS, Azure, or GCP)
• Knowledge of modern data warehousing solutions (e.g., BigQuery)
• Solid software engineering practices (version control, code reviews, testing)
• Strong communication skills in English (B2+ level)
• Around 5–7 years of relevant experience

Responsibilities

You will be responsible for operationalizing generative AI and large language models (LLMs) to drive efficiency, innovation, and smarter decision-making across the organization. Working closely with cross-functional teams, you will ensure seamless integration of AI technologies into business processes.

Key responsibilities

• Design and maintain ontologies, semantic standards, and data models to improve data interoperability
• Collaborate with Data Engineering, BI, and Data Management teams to ensure consistent modeling practices
• Define and enforce best practices for LLM operations, lifecycle management, and responsible AI usage
• Deploy and scale LLM-based solutions in production environments
• Lead innovation efforts by tracking AI trends and translating them into practical solutions
• Support integration of AI into supply chain processes, enabling predictive and automated decision-making