Senior ML Engineer

Job description / Role

Description

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

• 5+ years of experience in MLOps, ML infrastructure, or related engineering roles.
• Demonstrated experience deploying machine learning models into production.
• Strong programming skills in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
• Proficiency with containerization and orchestration (Docker, Kubernetes).
• Experience with cloud ML platforms (AWS, GCP, or Azure).
• Familiarity with workflow orchestration (Airflow, Prefect, Mage, or equivalents).
• Understanding of CI/CD automation for ML workloads.
• Working knowledge of DevOps concepts (infrastructure-as-code, logging/monitoring, networking basics).
• Experience working on recommendation systems.

Benefits

• Comprehensive training & development programs.
• Performance-based bonus incentives.
• Flexible work from home options.