Machine Learning Engineer

Quick Facts

• Role: Machine Learning Engineer

• Focus: Production machine learning pipelines for price optimization in a hybrid cloud environment

• Key tools: Python, PySpark, Airflow, Azure ML, Terraform, Docker, Kubernetes, CI/CD, MLflow, FastAPI

Description

You will build and maintain production-ready machine learning pipelines for price optimization in a hybrid environment (cloud and on-prem clusters). The work includes implementing the full ML lifecycle—from data preparation to automated deployment and continuous monitoring—and exposing reusable model capabilities through APIs. Collaboration with data science teams supports productionization of ML models and optimization algorithms across multiple business domains.

Role description / Responsibilities

• Implement the end-to-end machine learning lifecycle, including automated deployment and continuous monitoring of data and models in production

• Develop PySpark data pipelines for loading and transforming large datasets to produce features for modeling and analytics

• Provision cloud resources for model development in Azure ML using Infrastructure as Code with Terraform

• Select appropriate architectural patterns to address business problems

• Build robust and maintainable solutions for both cloud and on-prem to deliver models reliably and quickly

• Help establish a mature DevOps culture and create solutions reusable across multiple business domains

Requirements

• 5+ years of hands-on machine learning engineering experience

• Strong proficiency in writing high-quality Python code

• Experience with orchestration tools such as Airflow

• Knowledge of Spark or other distributed data processing tools

• Experience using the Kubernetes ecosystem

• Advanced expertise in Azure and Docker

• Ability to collaborate in an engineering team and contribute to system design

• English proficiency (B2/C1)

• Strong communication and mentoring capabilities

• Proactive, responsible mindset with a strategic, big-picture perspective

• Availability to work in a hybrid model from time to time

Benefits

• Flexible hybrid work model

• Home office reimbursement

• Language lessons

• Private healthcare

• Training package and in-house training

• Optional benefits for B2B contractors