MLops Engineer
Cyprus / Larnaca
Job Description
We are seeking a talented and experienced MLOps Engineer to join our dynamic team. As an MLOps Engineer, you will play a crucial role in bridging the gap between machine learning (ML) development and production deployment, ensuring the seamless integration of ML models into our operational systems. You will be responsible for automating and streamlining the end-to-end ML lifecycle, from model development and training to deployment and monitoring. The ideal candidate will have a strong background in both machine learning and operations, with a focus on building scalable and efficient ML pipelines.
Responsibilities
- Supervise Data Scientists: Work closely with data scientists to understand model requirements, implement machine learning models, and optimize them for deployment.
- Build and Maintain ML Pipelines: Design, develop, and maintain robust and scalable ML pipelines for model training, testing, and deployment, ensuring reproducibility and reliability.
- Deployment and Monitoring: Implement automated deployment strategies for ML models, and establish monitoring and alerting systems to track model performance, data drift, and system health.
- Infrastructure and Environment Management: Manage and optimize ML infrastructure, including cloud resources, containers, and orchestration tools. Ensure consistency and reproducibility across development, testing, and production environments.
- Automation and Continuous Integration/Continuous Deployment (CI/CD): Implement CI/CD pipelines to automate the testing and deployment of ML models, reducing time-to-production and ensuring a smooth workflow.
- Collaborate with DevOps and IT teams: Work closely with DevOps and IT teams to integrate ML components into existing systems, ensuring security, scalability, and reliability.
- Documentation and Knowledge Sharing: Create and maintain documentation for MLOps processes, best practices, and standards. Facilitate knowledge sharing and training sessions to educate team members on MLOps principles.
Requirements
- Educational Background: Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
- Technical Skills:
- Proficiency in programming in Python
- Strong understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes).
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
- Expertise in version control systems (e.g., Git) and collaborative development practices.
- MLOps Tools:
- Knowledge of CI/CD tools (e.g., Jenkins, GitLab CI) and versioning tools.
- Problem-solving and Collaboration:
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.
- Continuous Learning: A proactive mindset for staying updated on the latest developments in machine learning, MLOps, and related technologies.
The Following Will Be Considered an Advantage
- Professional certifications in MLOps, DevOps, or cloud platforms.
- Previous experience in deploying and managing ML models in a production environment.
- Knowledge of data engineering principles and tools.
- Experience with MLOps tools such as MLflow, Kubeflow, or Apache Airflow will be considered as advantage
- Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and model deployment tools will be considered as advantages.
Benefits
- Bi‑Monthly Free Meal Days on premises Enjoy a company‑provided meal twice every month—great food, zero cost.
- Provident Fund Contribution Become eligible for employer‑matched savings after just six months of service.
- Generous Paid Time‑Off 22 days of annual holiday leave plus all public holidays in Cyprus
- 6 paid sick days each year for peace of mind when you need it most
- Birthday Gift A thoughtful present from the company to help you celebrate your special day.
- Free Parking Complimentary on‑site parking so you can arrive stress‑free and on time.
- Flexible Timetable Adjustable working hours to help you balance professional and personal commitments.
- Professional Certification Support We reimburse approved certification fees.
- Flexible working (Hybrid, Remote mode available)