ML Platform Engineer

We are a team of experts and tech enthusiasts on a mission to bring together the best minds in IT services and analytics. Our goal? To build cutting-edge IT and Analytical Hubs that empower our partners to become truly data-driven organizations.

Currently, we are looking for a hands-on ML Platform Engineer to support one of hubQuest’s partners in developing a Global Analytics unit – a centralized, cross-functional team dedicated to strengthening data-driven decision-making and creating smart data products for day-to-day operations.

This is a unique opportunity to work on a high-impact internal platform used by Data Scientists and ML Engineers worldwide to seamlessly build, run, and productionize machine learning pipelines. The role is strongly MLOps-oriented, combining deep software engineering expertise with a strong understanding of the ML lifecycle, infrastructure, and scalable platform design.

About the Team

The Global Analytics team is an innovative, diverse collective of Data Scientists, ML Engineers, MLOps Engineers, Data Engineers, BI Specialists, Software Developers, UX Designers, and more. With a presence across three continents and five countries, the team fosters collaboration, drives innovation, and ensures reliability, transforming the organization into a leader in data-driven decision-making.

Your Responsibilities

• Build and evolve the MLOps framework for running, monitoring, and deploying ML pipelines.

• Design and maintain platform components to automate and simplify ML workflows.

• Develop production-grade ML libraries, algorithms, and CLI tooling.

• Build and maintain a FastAPI backend to expose results and trigger simulations/optimizations.

• Automate workflows using Databricks Workflows and integrate with the Azure stack (Azure ML, ADF, Azure Functions, ADLS, Web Apps, Redis, etc.).

• Develop and enhance end-to-end machine learning pipelines.

• Optimize data ingestion and feature engineering processes for large-scale applications.

• Contribute to CI/CD processes with Azure DevOps.

• Collaborate closely with Data Scientists and Engineers to improve the developer experience.

• Conduct code reviews and ensure best engineering practices (testing, standards, modularity).

What We’re Looking For

Must-have:

• 5+ years of overall software engineering experience.

• At least 1 year as an MLOps or ML Engineer in production environments.

• Strong Python programming skills, especially in data-heavy contexts.

• Hands-on experience with ML infrastructure at scale.

• At least 1 year of data engineering experience.

• Strong knowledge of the ML lifecycle and workflows.

• Experience with Azure DevOps and CI/CD pipelines.

• Ability to design clean, modular APIs and internal tools.

• Fluent English (daily communication).

Nice-to-have:

• Experience with FastAPI or similar frameworks.

• Familiarity with MLflow, Databricks, or Azure ML pipelines.

• Experience with PySpark for large-scale data processing.

• Solid SQL knowledge for data exploration.

• Experience building internal platforms or tooling for ML/DS teams.

• Understanding of orchestration patterns and scalable ML infra.

What We Offer

• High-impact role focused on enabling ML at scale.

• Opportunity to grow in MLOps and ML platform development.

• Flexible remote or hybrid setup with a modern office in Warsaw.

• A global, diverse team with real ownership and no red tape.

• Benefits: private medical care, Multisport, access to online learning and certifications.

• Supportive, collaborative, and relaxed atmosphere.

Why Join Us?

If you're passionate about empowering ML teams through engineering, automating workflows, and building platforms that scale — apply now and join our mission at hubQuest!

Please add to your CV the following clause:

"I hereby agree to the processing of my personal data included in my job offer by hubQuest spółka z ograniczoną odpowiedzialnością located in Warsaw for the purpose of the current recruitment process.”

If you want to be considered in the future recruitment processes please add the following statement:

"I also agree to the processing of my personal data for the purpose of future recruitment processes.”