Senior AI Engineer: Databricks ML Pipelines & MLOps

EPAM Systems; Turkey; On-site; TRY 250,000 - 450,000; Full time

Benefits offered by this job

Private health insurance

Continuous upskilling

English courses

USD compensation package

Learning platforms access

Job summary

EPAM Systems is seeking a Senior AI Engineer to design, deploy and maintain scalable ML pipelines on the Databricks platform. You will deliver production-ready pipelines, automated training and retraining workflows, and CI/CD for ML systems.

The role requires Databricks expertise, PySpark, Python, MLflow, and experience with Azure Databricks, ADLS and Terraform. Strong communication in English is expected in a global team.

Qualifications

• 3+ years of ML engineering experience with end-to-end pipelines.
• Databricks platform expertise including notebooks, jobs and clusters.
• Proficient in Spark, PySpark, Python (pandas, scikit-learn).
• MLflow for tracking, registry and deployment; CI/CD for ML.

Responsibilities

• Design, build and maintain end-to-end ML pipelines on Databricks.
• Create data ingestion, preprocessing, feature engineering and training workflows.
• Develop batch and real-time inference deployments with monitoring.
• Implement CI/CD pipelines for ML workflows using Azure DevOps and GitHub Actions.
• Manage model versioning, experiment tracking and reproducibility with MLflow.
• Optimize storage, partitioning, and cost using Terraform, ARM and Bicep.
• Provide logging, alerts and observability for high availability.

Skills

Databricks

PySpark

Python

Pandas

Scikit-learn

MLflow

CI/CD

Docker

REST APIs

Git

Azure Databricks

Azure Data Lake

Terraform

Monitoring

Education

Bachelor's degree in CS/Math/Engineering

Master's degree preferred

Tools

Azure DevOps

GitHub Actions

Databricks Repos

Terraform

ARM

Bicep

Job description

EPAM Systems is seeking a Senior AI Engineer to design, deploy and maintain scalable ML pipelines on the Databricks platform. You will deliver production-ready pipelines, automated training and retraining workflows, and CI/CD for ML systems.

The role requires Databricks expertise, PySpark, Python, MLflow, and experience with Azure Databricks, ADLS and Terraform. Strong communication in English is expected in a global team.