ML Operations Engineer

Job Description: • The Machine Learning Operations (MLOps) Engineer will support our AI/ML initiatives by streamlining the deployment, monitoring, and scaling of machine learning models in production environments. • Implement and maintain CI/CD pipelines for deploying machine learning models to production environments. • Ensure seamless integration of machine learning models into existing software systems. • Design and manage scalable infrastructure for training, testing, and serving machine learning models. • Automate data preprocessing, model training, and deployment workflows. • Monitor the performance of deployed models and systems, identifying and resolving issues proactively. • Optimize model inference latency, scalability, and resource utilization. • Work closely with data scientists, software engineers, and product teams to understand requirements and deliver operational solutions. • Collaborate with DevOps and cloud engineering teams to ensure infrastructure reliability and security. • Maintain version control for datasets, models, and code. • Implement best practices for data and model governance, ensuring compliance with organizational and regulatory requirements. • Stay updated with the latest trends in MLOps tools, frameworks, and practices. • Recommend and implement improvements to the MLOps processes and infrastructure. Requirements: • Education Required: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. • Experience Required: 2-3 years of hands-on experience in MLOps, DevOps, or related roles. • Experience with MLOps tools and platforms like MLflow, Kubeflow, or SageMaker. • Experience with feature stores and model versioning systems. • Experience in building CI/CD pipelines using tools like Jenkins, GitLab CI, or similar. • Knowledge of: Proficiency in Python and familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes). • Strong understanding of containerization and orchestration tools (e.g., Docker, Kubernetes). • Familiarity with distributed computing frameworks (e.g., Apache Spark). • Knowledge of cloud platforms such as AWS, Azure, or Google Cloud. • Solid understanding of model monitoring, logging, and debugging tools. • Familiarity with database technologies and data pipelines (SQL, NoSQL, ETL/ELT processes). Benefits: