Senior DevOps Engineer - MLOps Focus

December 3

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Description

• Design, implement, and maintain scalable CI/CD pipelines tailored for AI/ML projects. • Automate the deployment and monitoring of ML models in production using tools like Kubernetes, Docker, or similar containerization platforms. • Develop workflows for continuous model training, validation, and deployment. • Collaborate with Data Scientists to ensure seamless integration of models into production environments. • Implement versioning and tracking systems for datasets, models, and training pipelines (e.g., MLflow, DVC). • Build systems to monitor the performance of ML models in production and trigger retraining when necessary. • Optimize resource utilization for training and inference workloads on cloud or hybrid environments. • Partner with cross-functional teams, including Data Scientists, Engineers, and Product Managers, to align infrastructure goals with business needs. • Document processes and educate team members on best practices for DevOps and MLOps.

Requirements

• Proficiency in Python, Bash, or similar scripting languages. • Experience with CI/CD tools (e.g., Jenkins, GitLab CI/CD, CircleCI). • Strong understanding of containerization and orchestration tools (Docker, Kubernetes). • Familiarity with cloud platforms (AWS, Azure, GCP) and related services for ML workloads. • Hands-on experience with MLOps tools like Kubeflow, MLflow, DVC, or TensorFlow Serving. • Knowledge of ML model lifecycle management. • Experience with monitoring tools for models (e.g., Prometheus, Grafana, SageMaker Model Monitor). • Strong problem-solving skills and a proactive mindset. • Excellent communication skills to collaborate with technical and non-technical stakeholders. • Good spoken and written English communication skills.

Benefits

• Competitive salary and benefits package. • Opportunity to work on cutting-edge AI/ML projects. • A collaborative and innovative work environment. • Professional growth and development opportunities.