Data Scientist

Please note - hybrid from Warsaw (3 days from the office, 2 remotely).

About us:

We help businesses use AI and digital tools to work better and grow faster, especially in private capital markets. Our Core Platform improves workflows and gives useful insights with AI. Olympus Software is a fast, smart cloud system that grows with your needs. The Pantheon Suite offers flexible tools to manage and improve business performance. With over 10 years of experience, we know how to turn technology into real business value.

About the Role

We are looking for an AI Engineer to join our PaaS (Platform-as-a-Service) Customer Delivery team in Warsaw. In this role, you will focus on designing and delivering client-specific AI solutions built on top of our platform, while contributing to the development of reusable machine learning components and deployment infrastructure.

You will collaborate with data scientists, engineers, and customer-facing teams to adapt AI capabilities to diverse enterprise use cases. This role is ideal for someone who enjoys hands-on model development, MLOps implementation, and applied problem-solving in real customer contexts.

Key Responsibilities

• Customize and deploy AI/ML models tailored to client requirements using internal platform capabilities and APIs.

• Develop, maintain, and scale end-to-end ML pipelines from experimentation through production.

• Collaborate with customer implementation teams to scope and translate business needs into machine learning tasks.

• Package models as reproducible services or microservices that can be integrated with customer-facing products.

• Optimize data preprocessing, feature engineering, and inference runtimes to ensure scalable solutions.

• Contribute to internal frameworks and tools that support ML deployment, monitoring, and retraining workflows.

• Work with stakeholders to document, explain, and validate models with both technical and non-technical audiences.

Qualifications

Required

• 3–5 years of experience in AI/ML engineering or applied machine learning.

• Proficiency in Python and experience with ML libraries such as scikit-learn, TensorFlow, or PyTorch.

• Experience building and deploying models in production environments.

• Solid understanding of ML lifecycle management, including testing, versioning, and monitoring.

• Strong knowledge of data processing tools and techniques.

• Ability to work cross-functionally in collaborative, delivery-driven teams.

Preferred

• Experience with MLOps tooling (e.g., MLflow, Kubeflow, SageMaker, or Vertex AI).

• Familiarity with REST APIs and cloud environments (GCP, AWS, or Azure).

• Exposure to NLP, forecasting, or recommender systems.

• Experience in client-facing roles or implementation-focused solution engineering.