Senior MLOps Engineer

This is a fully remote position, open to applicants in Greece.

📋 Description

• Take ownership of the reliability, scalability, and automation of machine learning pipelines.

• Oversee both the infrastructure and the lifecycle of machine learning.

• Utilize Docker to containerize pipeline services and deploy them on AWS.

• Create scalable, event-driven executions for data preparation, model training, and prediction tasks.

• Develop and enhance CI/CD pipelines utilizing GitHub Actions.

• Enhance observability through effective logging, metrics, and alert notifications.

• Fortify the model lifecycle with reproducible training environments, experiment tracking using MLflow, model versioning, and deployment strategies.

• Advance and scale production pipelines that serve customers.

• Architect cloud solutions and lead platform modernization from proposal to production.

• Manage projects from start to finish and deliver incrementally without interrupting live systems.

• Collaborate with data scientists and DevOps engineers throughout the stack.

• Optimize workflows for training, evaluation, and deployment.

⛳️ Requirements

• Minimum of 5 years of experience managing production Python systems.

• Strong foundation in software engineering principles, including testing, version control, code review, and CI/CD practices.

• Practical experience with Docker, including writing and optimizing Dockerfiles, conducting multi-stage builds, and debugging containers in a production environment.

• Robust cloud experience, especially in compute and storage.

• Familiarity with AWS services such as EC2, ECS, S3, EFS, CloudWatch, and IAM.

• Proven experience in creating automated build, test, and deployment pipelines with GitHub Actions or similar tools.

• Knowledge of document and relational databases, including MongoDB and PostgreSQL.

• Ability to securely connect services to databases within containerized settings.

• Experience supporting the machine learning lifecycle in production, including experiment tracking and model management with MLflow or equivalent tools.

• Familiarity with reproducible training pipelines and model deployment techniques.

• Knowledge of modern Python environment and configuration tools, such as uv, pydantic, and Hydra/OmegaConf.

• Experience using workflow orchestrators like SageMaker Pipelines or Prefect.

• Comfort level with TensorFlow, scikit-learn, LightGBM, and geopandas.

• Exposure to geospatial data or GIS tools such as geopandas, PostGIS, or ArcGIS is an advantage but not mandatory.

• Commitment to cost efficiency, including optimizing compute resources and selecting appropriate storage solutions.

🏝️ Benefits

• Full-time employment.

• Fully remote work setup.

• Opportunity to contribute to AI-based infrastructure that supports water utilities and municipalities.

• Collaboration with data scientists and DevOps engineers across the entire stack.

• Work on a multi-award-winning solution utilized by customers globally.