ML Ops Engineer

Job Description:

• We are seeking an experienced MLOps Engineer to design implement and maintain scalable machine learning pipelines and infrastructure
• The ideal candidate will bridge the gap between data science and production systems ensuring robust deployment monitoring and lifecycle management of ML models

Key Responsibilities:

• Key Responsibilities
• Develop and maintain CI CD pipelines for ML models and data workflows
• Automate model training testing deployment and rollback processes
• Implement monitoring and alerting for model performance and data drift
• Optimize infrastructure for cost scalability and reliability cloud or hybrid environments
• Collaborate with data scientists and software engineers to integrate ML models into production
• Ensure compliance with security governance and reproducibility standards

Technical Requirements:

• Required Skills
• Strong proficiency in Python and ML frameworks TensorFlow PyTorch Scikit learn
• Experience with containerization Docker and orchestration Kubernetes
• Familiarity with cloud platforms AWS Azure GCP and ML services
• Expertise in CI CD tools GitHub Actions Jenkins Argo
• Knowledge of feature stores model registries and ML observability tools
• Understanding of data versioning and experiment tracking MLflow DVC

Additional Responsibilities:

• Experience
• 5 8 years of experience in software engineering or data engineering with at least 3 years in MLOps
• Preferred Qualifications
• Experience with large scale ML systems and distributed training
• Familiarity with GenAI model deployment and optimization
• Strong problem solving and debugging skills in production environments

Preferred Skills:

Technology->AI-AI Engineering->MLOps,Technology->Cloud Platform->Azure Devops,Technology->Cloud Platform->GCP Devops,Technology->Cloud Platform->Google Cloud - Architecture,Technology->Container Platform->Docker,Technology->AI-Data science->PYTHON