Agentic AI engineer for recommendation systems

We are an implementation and consulting company. We specialize in areas such as IT rescue, digital transformation, and maintenance. Together, we create state-of-the-art digital solutions, primarily for clients in healthcare, banking, insurance, e-commerce, FMCG, and retail. We combine innovation with a practical approach to solving technological problems. Our team is made up of IT enthusiasts who value growth, modern technologies, and a friendly work environment.

• Acting as a trusted technical advisor to clients — solving complex machine learning challenges and guiding them through the process of practical system implementation.
• Designing the architecture and implementing multi-step workflows based on Agentic AI.
• Delivering tailored production solutions in collaboration with clients, partners, and product teams.
• Designing and building data pipelines and infrastructure for distributed machine learning and recommendation systems.
• Implementing MLOps solutions, including data validation, model monitoring, and lifecycle management in a production environment.
• Creating and optimizing ETL/ELT processes in analytics environments and data warehouses.
• Creating dedicated scripts and software to support advanced artificial intelligence and ML systems.

• At least 7 years of hands-on experience working with recommendation systems, data pipelines, or distributed machine learning.
• Practical experience in machine learning (ML), artificial intelligence (AI), technical consulting, and customer service.
• Knowledge of programming and scripting languages such as Python, Perl, C, C++, or Java.
• Experience with Vertex AI, MLOps, Document AI, and Generative AI.
• Experience building multi-step Agentic AI workflows using ADK, Agent Engine, LangChain, and AgentSpace.
• Understanding of the practical aspects of deploying ML models to production (feature extraction and definition, data validation, monitoring, and model management).
• Knowledge of deep learning and ML frameworks such as TensorFlow, PyTorch, or XGBoost.
• Knowledge of data warehouse architecture, ETL/ELT processes, and analytics environments (Apache Beam, Hadoop, Spark, Hive).

• Work on client projects and proprietary products
• Real influence on business and technology solutions within the projects being delivered
• Training and support in defining a career development path
• Company events, access to medical services, and sports packages
• Employment under a B2B contract
• Remote/hybrid work (Warsaw)
• A short recruitment process