Data & AI Warsaw Tech Summit 2026: Machine Learning Engineer – From Models to Production

Many AI solutions still fall short when it comes to understanding and reasoning about complex financial concepts. The real challenge is about how financial knowledge is represented and shared with machines. Why does AI still misunderstand basic banking terms despite having access to vast amounts of data?
How can AI truly understand financial concepts? Using the Financial Industry Business Ontology (FIBO) as an example of structured domain knowledge, we will discuss how formal, machine-readable definitions can provide the contextual foundation AI needs. By analysing selected conversational banking scenarios and example solutions, we will invite participants to reflect together on what the right semantic layer for AI in banking should look like.
Join us to discover why the next leap in AI for banking isn’t just about more data or better models, but about building a structured understanding of financial meaning.

We are looking for a Machine Learning Engineer to design, build, and deploy scalable machine learning solutions. In this role, you will work closely with data scientists, data engineers, and product teams to bring ML models into production and ensure their performance, reliability, and scalability.

• Design, develop, and deploy machine learning models into production

• Build and maintain scalable ML pipelines and workflows

• Collaborate with data scientists to operationalize models (MLOps)

• Optimize model performance, scalability, and latency

• Monitor, evaluate, and retrain models in production

• Work with large datasets and feature engineering processes

• Implement best practices for versioning, testing, and deployment of ML models

• Integrate ML solutions into existing systems and applications

• Document models, pipelines, and processes

• Proven experience as a Machine Learning Engineer or similar role (X+ years)

• Strong programming skills in Python (or similar language)

• Experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)

• Solid understanding of machine learning algorithms and statistics

• Experience with data processing tools (e.g., Pandas, Spark)

• Familiarity with MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow)

• Experience with cloud platforms (AWS, Azure, or GCP)

• Knowledge of APIs and microservices architecture

• Strong problem-solving and communication skills

• Experience with deep learning and NLP or computer vision

• Familiarity with Docker and Kubernetes

• Experience with CI/CD pipelines for ML

• Knowledge of data engineering concepts and tools

• Experience with real-time or streaming data systems