MLOps Engineer

Job Description : As an MLOps Engineer, you will be responsible for designing and implementing scalable and automated pipelines for deploying and managing machine learning models in production environments. You will focus on optimizing model performance, ensuring seamless integration with existing systems, and continuously monitoring model health to deliver impactful AI-driven solutions. Responsibilities : - Analyze and optimize machine learning models for improved performance (speed, latency, resource utilization) and reduced deployment time. - Implement techniques such as model quantization, pruning, and efficient inference strategies. - Design, build, and implement end-to-end automation pipelines for deploying machine learning models to various production environments (cloud, on-premise, edge). - Utilize infrastructure-as-code (IaC) principles for managing deployment infrastructure. - Work closely with data scientists throughout the model development lifecycle to understand model requirements and ensure a smooth transition from research and experimentation to production. - Collaborate on defining deployment strategies and performance metrics. - Establish robust monitoring systems to track model performance (accuracy, drift, latency) and infrastructure health in production environments. - Implement alerting mechanisms for anomalies and performance degradation. - Troubleshoot and resolve issues related to deployed ML models. - Manage model versioning and rollback strategies. - Design and implement Continuous Integration/Continuous Delivery (CI/CD) pipelines specifically tailored for machine learning models, including automated testing, building, and deployment processes. - Manage and maintain the infrastructure required for training and deploying machine learning models, leveraging cloud platforms (AWS, GCP, Azure) and containerization technologies. - Ensure the security and compliance of ML deployments, adhering to best practices and organizational policies. - Create and maintain comprehensive documentation for deployment pipelines, monitoring systems, and operational procedures. Skills Required : - Strong knowledge and practical experience with popular machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, etc. - Proven experience in optimizing machine learning models for performance and efficiency, and deploying them to production environments. - Hands-on experience with at least one major cloud platform (AWS, GCP, Azure) and its relevant services for machine learning and infrastructure. - Experience designing and implementing CI/CD pipelines specifically for machine learning models using tools like Jenkins, GitLab CI, CircleCI, or cloud-native CI/CD services. - Proficiency in Python and other relevant scripting languages. - Strong understanding of Linux operating systems. Preferred Qualifications : - ML Model Performance Metrics & Monitoring : Deep understanding of various ML model performance metrics and experience implementing monitoring solutions using tools like Prometheus, Grafana, or cloud-specific monitoring services. - Familiarity with containerization tools like Docker and container orchestration platforms like Kubernetes. - Experience with IaC tools such as Terraform or CloudFormation. - Basic understanding of data engineering principles and tools for data pipelining and preparation. (ref:hirist.tech)

Location: in, IN

Posted Date: 4/19/2025