Senior ML Engineer — Remote, Enterprise AI Pipelines

emagine Polska; United States; Remote; USD 60,000 - 67,000; Full time

Job summary

emagine Polska is seeking a seasoned ML Engineer to own the complete ML lifecycle—from framing problems and prototyping to deployment and monitoring. You will design data pipelines and ensure reliability and reproducibility across enterprise data sources, including ERP and PLM, while advancing MLOps practices like CI/CD and automated retraining.

The role requires 5+ years in ML/DS, strong Python and ML frameworks, and experience with cloud, Docker, Kubernetes, and modern data platforms.

Qualifications

• 5+ years in Machine Learning Engineering, Data Science or Applied ML.
• Proven experience deploying ML models at scale in production.
• Advanced Python and ML frameworks (scikit-learn, TensorFlow, PyTorch, Spark MLlib).
• Experience with Databricks, Snowflake, Spark or Hadoop - strongly preferred.
• Strong SQL skills.
• Cloud experience (Azure preferred), Docker, Kubernetes.
• Git, CI/CD, JIRA, Confluence.
• Nice to have: retail, supply chain, manufacturing or e-commerce background, and forecasting projects.

Responsibilities

• Own the ML lifecycle: from problem framing and prototyping to deployment and monitoring.
• Design and improve data pipelines that are reliable and reproducible.
• Drive MLOps best practices: CI/CD for ML, monitoring, automated retraining.
• Work with data scientists, engineers and business stakeholders to turn goals into ML solutions.
• Define KPIs and keep improving accuracy, efficiency and scalability.

Skills

Python

ML frameworks

Databricks

Snowflake

Spark

SQL

Azure

Docker

Kubernetes

Git

CI/CD

JIRA

Confluence

Tools

Databricks

Snowflake

Spark

Job description

emagine Polska is seeking a seasoned ML Engineer to own the complete ML lifecycle—from framing problems and prototyping to deployment and monitoring. You will design data pipelines and ensure reliability and reproducibility across enterprise data sources, including ERP and PLM, while advancing MLOps practices like CI/CD and automated retraining.

The role requires 5+ years in ML/DS, strong Python and ML frameworks, and experience with cloud, Docker, Kubernetes, and modern data platforms.