ML OPs Engineer

ML OPs Engineer

The MLOps Engineer plays a critical role in bridging the gap between machine learning model development and operational deployment. You will be responsible for designing implementing and maintaining scalable ML pipelines and infrastructure to ensure smooth deployment monitoring and retraining of models in production environments.

Key Responsibilities

Develop and maintain CI CD pipelines for ML models
Automate model deployment monitoring and performance tracking
Collaborate with data scientists to ensure models are production ready
Implement tools for model versioning testing and validation
Ensure compliance with data governance and security standards
Optimize ML workflows for scalability and cost efficiency
Troubleshoot and resolve issues related to model performance and infrastructure
Stay updated with the latest MLOps tools and best practices
Support continuous improvement by identifying and solving opportunities

Required Skills and Qualifications

Bachelors or Masters degree in Computer Science Data Science or related field
3 plus years of experience in MLOps DevOps or related roles
Strong programming skills in Python familiarity with ML frameworks like TensorFlow PyTorch or Scikit learn
Experience with cloud platforms AWS Azure GCP
Proficiency in containerization Docker and orchestration Kubernetes
Knowledge of CI CD tools such as Jenkins GitHub Actions
Familiarity with monitoring tools Prometheus Grafana
Excellent problem solving and communication skills

Preferred Qualifications

Bachelors or Masters degree in Computer Science Data Science or related field