Senior MLOps Engineer (relocation to Cyprus)

Senior MLOps Engineer will design and build production-grade ML systems on a modern cloud AI platform. The role is focused on writing code, creating and maintaining end-to-end ML pipelines and turning research and PoCs into scalable, high-quality production services used by real applications and business users. Engineers will work closely with data scientists and data engineers to integrate models with data platforms, APIs and cloud services with a strong emphasis on automation, reliability and performance.

Responsibilities

• Build and maintain end-to-end ML pipelines (training, validation, model registry, deployment) using Azure, Palantir Foundry or similar platforms
• Design scalable data pipelines for feature engineering, model training and real-time/batch inference using Python, Spark, SQL and cloud data platforms
• Operationalize AI/ML workflows by integrating models with data APIs, ML pipelines, and production engineering practices
• Optimize model and data performance through validation, monitoring, automated testing and experimentation
• Collaborate with data scientists and ML engineers to move prototypes into robust production systems

Requirements

• Proven experience in MLOps or Machine Learning Engineering with a strong track record of building and maintaining end-to-end ML pipelines
• Proficiency in engineering scalable ML pipelines for training, validation, deployment and model registry
• Advanced programming skills in Python for developing and operationalizing ML systems
• Hands-on experience with cloud data platforms (e.g., Azure, Palantir Foundry or similar)
• Expertise in deploying machine learning models for both batch and real-time inference scenarios
• Solid understanding and practical experience with CI/CD practices tailored for ML workflows

Nice to have

• Experience with feature stores and data versioning tools
• Knowledge of model monitoring, drift detection and automated validation frameworks
• Exposure to Generative AI (GenAI) or Large Language Model (LLM) workflows
• Familiarity with distributed data processing frameworks such as Apache Spark
• Experience with experiment tracking frameworks and tools

We offer/Benefits

• Private healthcare insurance
• Global travel medical and accident insurance
• Regular performance assessments
• Referral bonuses
• Family friendly initiatives
• Learning and development opportunities including in-house training and coaching, professional certifications, over 22,000 courses on LinkedIn Learning Solutions and much more
• *All benefits and perks are subject to certain eligibility requirements

Why Choose Us

2024 Best Place to Work 2024 2024 Sitecore's Partner Experience Awards

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