MLOps Engineer

MLOps Engineer

A leading security company in the Shfela region is seeking for an experienced MLOps Engineer to join the DevOps team. As part of our dynamic and innovative organization, you will play a meaningful role in the integration of machine learning operations (MLOps) into our DevOps workflows, ensuring seamless deployment and management of machine learning models.

Role Description:

• Collaboration: Collaborating with cross-functional teams to establish best practices and standards for MLOps within our DevOps environment.
• Automation: Designing and implementing ML pipelines and workflows to automate model training, testing, and deployment processes.
• Integration: Integrating machine learning models into production environments and ensuring their scalability, reliability, and performance.
• Development: Developing tools and frameworks to facilitate the management and monitoring of machine learning experiments and deployments.
• Support: Providing technical guidance and support to data scientists and software engineers on MLOps-related tasks and issues.
• Innovation: Continuously evaluating and adopting new technologies, tools, and methodologies to enhance our DevOps and MLOps capabilities.

Job Requirements:

• Over four years of hands-on experience as a DevOps engineer, with a strong background in software development and system administration.
• Deep understanding of machine learning and deep learning concepts, frameworks (such as PyTorch, TensorFlow, and Keras), and model development lifecycle.
• Proven experience in building and managing ML pipelines, workflows, and lifecycle processes, from data preprocessing to model deployment.
• Proficiency in Python programming, with the ability to develop code for automating MLOps tasks and processes.
• Familiarity with data engineering tools and technologies, such as Apache Spark, Kafka, and Data Pipelines.
• Knowledge of MLOps tools and platforms, including ClearML, for experiment tracking and deployment automation.
• Strong expertise in building and optimizing CI/CD pipelines, utilizing tools like Jenkins, GitHub/GitLab, and Artifacts management.
• Solid understanding of microservices architectures, containerization technologies (e.g., Docker, Kubernetes), and cloud platforms (AWS, GCP, Azure).
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders.