Machine Learning Operations (MLOps) Engineer

About the Role

Our client is seeking an experienced Machine Learning Operations (MLOps) Engineer to join their innovative team, working entirely remotely. This role is crucial for bridging the gap between machine learning model development and production deployment, ensuring efficient, reliable, and scalable ML systems. You will be responsible for building and managing the infrastructure and workflows that support the entire ML lifecycle, from data preparation to model training, deployment, monitoring, and retraining. This is an exciting opportunity for an engineer passionate about automating and optimizing ML processes to have a significant impact on the deployment of cutting-edge AI technologies in a collaborative, remote-first culture.

Key Responsibilities

• Design, build, and maintain CI/CD pipelines for machine learning models.
• Develop and manage infrastructure for model training, deployment, and serving.
• Implement monitoring and alerting systems for ML models in production.
• Automate data pipelines and model retraining processes.
• Collaborate with data scientists and software engineers to streamline the ML lifecycle.
• Ensure the scalability, reliability, and security of ML systems.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in software engineering, DevOps, or MLOps.
• Strong proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch).
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with CI/CD tools (e.g., Jenkins, GitLab CI) and infrastructure as code (e.g., Terraform).
• Understanding of machine learning concepts and model lifecycle management.

Benefits

• Competitive salary and bonus structure.
• Comprehensive health, dental, and vision insurance.
• Fully remote work environment with flexible scheduling.
• Generous paid time off and professional development budget.
• Opportunity to work on advanced MLOps solutions and shape ML deployment strategies.