MLOps / Data Engineer

Quick Facts

• Role: MLOps / Data Engineer

• Work mode: Flexible hybrid (1x per week from the client’s Warsaw office)

• Project focus: MLOps and Machine Learning for predictive models and decision engines in production

Description

Join an MLOps and Machine Learning project focused on developing modern processes that support building, deployment, and maintenance of predictive models and decision engines. You will be responsible for automating and optimizing the model lifecycle, delivering scalable and reliable solutions in cloud environments. The work combines Data Science, Data Engineering, and MLOps, with an emphasis on effective deployment of AI solutions into production.

Responsibilities

• Design, build, and evolve MLOps environments supporting deployment, automation, and maintenance of Machine Learning models and decision engines

• Develop, implement, and monitor processes across the full lifecycle of analytical models

• Build and maintain CI/CD processes and automation for ML solution deployments following DevOps and MLOps best practices

• Design and implement processes on cloud/analytics platforms (Azure, Fabric, SAS Viya) for the MLOps team

• Analyze the platform’s development needs and implement improvements for building decision engines, predictive models, and integrating on-prem and cloud environments

• Evaluate new cloud technologies and services and recommend their use within the Machine Learning and AI architecture

Requirements

• Higher education in Data Science, econometrics, statistics, mathematics, physics or related fields

• Minimum 2 years of experience in MLOps, Machine Learning, or Data Engineering

• Strong knowledge of Azure and the Microsoft ecosystem

• Practical Python programming experience

• Knowledge of Git and CI/CD processes

• Experience working with analytics and Machine Learning platforms (e.g., Azure ML, Databricks, SAS Viya or similar)

• Knowledge of deployment, monitoring, and management of the ML model lifecycle

• Ability to analyze new technologies and recommend business-relevant applications

Benefits

• Real impact on the development and deployment of AI and Machine Learning solutions used in production

• Growth opportunities at the intersection of MLOps, Data Engineering, and Data Science in a modern cloud environment