Senior MLOps Engineer

Salla is on the lookout for a talented Senior MLOps Engineer to help streamline our machine learning lifecycle by implementing best practices and innovative solutions. In this role, you will work closely with data scientists, software engineers, and stakeholders to deploy robust machine learning models and maintain efficient operations.

Responsibilities:

• Design, build, and maintain scalable MLOps pipelines that foster collaboration between data scientists and engineering teams.
• Implement and manage workflows for model training, validation, deployment, and monitoring.
• Utilize cloud-based platforms (AWS, GCP, or Azure) to provision and manage machine learning resources.
• Develop tools and frameworks that assist in continuous integration and deployment of machine learning models.
• Collaborate with data scientists to understand model requirements and assist in feature engineering and selection.
• Monitor model performance in production, identify issues, and provide recommendations for model optimization.
• Establish best practices for versioning, testing, and documenting machine learning models.
• Stay updated with the latest developments in MLOps tools and methodologies.
• Strong communication skills to relay technical concepts to non-technical stakeholders.
• Excellent problem-solving skills and a proactive approach to addressing challenges.

Requirements

• A minimum of 5 years of experience in software engineering or DevOps with a focus on machine learning operations.
• Strong proficiency in programming languages such as Python and familiarity with machine learning frameworks (TensorFlow, PyTorch, etc.).
• Experience with cloud-based platforms like AWS, GCP, or Azure and their machine learning services.
• Hands-on experience with CI/CD processes specific to machine learning applications.
• Professional Experience tools such as Docker and Kubernetes.
• Strong understanding of database technologies (SQL and NoSQL) and data pipeline tools.
• Experience with monitoring tools and practices for ML models in production.
• Ability to work collaboratively in a cross-functional team environment.

Benefits

• Comprehensive Training & Development programs.
• Performance-based Bonus incentives.
• Flexible Work From Home options.