ML & GenAI Solutions Scientist

RITS Software House is a dynamically developing technology company within the RITS Group, offering innovative AI and machine learning solutions to clients in Poland and abroad.

• Education or professional experience in Machine Learning, Deep Learning, or Data Science.
• Practical knowledge of Large Language Models (LLM) and modern generative AI solutions.
• Experience documenting technical solutions, processes, and AI system architectures.
• Strong ability to translate complex technical topics into clear and organized documentation.
• Knowledge of AI Agents, agentic workflows, and prompt engineering.
• Skills in creating architectural diagrams, process maps, and technical specifications.
• Experience collaborating with R&D, ML Engineering, or Software Engineering teams.
• Analytical thinking and ability to synthesize knowledge from multiple sources.
• Excellent communication and teamwork skills with interdisciplinary teams.
• Understanding of ML/AI model building, evaluation, and deployment processes.

Nice to have:

• Experience with AI Agents, RAG, LLMOps, or MLOps projects.
• Familiarity with technical documentation tools such as Confluence, Notion, Jira, Draw.io, Lucidchart.
• Experience in R&D environments and innovative projects.
• Basic programming knowledge in Python and AI/ML ecosystem.

• Creating and maintaining technical documentation for AI, Machine Learning, and Large Language Models (LLM) solutions.
• Developing technical specifications, model architectures, and data strategy documents.
• Documenting LLM behaviors, prompt engineering strategies, and agentic workflows.
• Supporting system development and "agentization" by building a structured knowledge base for engineering teams.
• Translating business requirements into technical specifications for ML and development teams.
• Collaborating with architects, ML engineers, data scientists, and product teams on R&D projects.
• Preparing architectural diagrams, process maps, and data flow documentation.
• Defining standards and best practices for AI model evaluation, deployment, and maintenance.
• Building and expanding a central knowledge base supporting AI product development.
• Ensuring high-quality documentation to facilitate smooth AI solution deployment to production.

Nr ref.: 34/4/2026/AW/3413