ML Engineer

We are looking for a talented Senior Machine Learning Engineer to join our client’s growing team! Our client is expanding its AI & Data division and is looking for an experienced Machine Learning Engineer to design, develop, and deploy advanced ML solutions that power automation and digital transformation initiatives. This role combines hands-on engineering with technical leadership, focusing on creating scalable, production-ready ML systems that support real-world business processes.

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Our client is a leading Greek company specializing in information management and digital transformation. It provides solutions for document management, intelligent data processing, and secure information storage. Through advanced technologies and automation, the company helps organizations optimize workflows, ensure compliance, and improve operational efficiency across industries.

Key Responsibilities

• Design, develop, and integrate machine learning models into production environments.
• Build and maintain data pipelines and model training workflows to ensure reliable and automated processes.
• Develop and enhance the company’s MLOps infrastructure for model lifecycle management, automation, and monitoring.
• Write production-grade Python code and build APIs or services that expose ML models to business applications.
• Continuously monitor, evaluate, and retrain models to maintain accuracy and performance.
• Collaborate with data engineers, annotation teams, software developers, and product managers to align solutions with business objectives.
• Participate in code reviews, documentation, and the definition of engineering best practices.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or a related discipline.
• 4 + years of hands-on experience developing and deploying machine learning models.
• Proven people management or team leadership experience.

  Strong proficiency in Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Solid understanding of MLOps tools and practices (e.g., MLflow, Kubeflow, Airflow).
• Working knowledge of cloud platforms such as AWS, GCP, or Azure.
• Excellent analytical thinking, problem-solving, and communication skills.
• Fluent in English, both written and spoken.

Nice-to-Have Skills

• Experience with document understanding, OCR, or NLP models.
• Familiarity with containerization (Docker, Kubernetes) and CI/CD for ML pipelines.
• Exposure to data governance, model monitoring, or AI observability tools.

What They Offer

• You’ll be part of a growing AI team within a well-established organization that is transforming its services through technology.
• You’ll work on end-to-end ML systems, from data ingestion to deployment, with real business impact across automation and document intelligence domains.
• If you’re passionate about building scalable ML products and want to see your models running in production, this is the right environment for you.