MLOps Engineer

Job Description

POSITION: MLOps Engineer LOCATION: Bangalore (Hybrid) Work timings - 12 pm - 9 pm Budget - Maximum 20 LPA ROLE OBJECTIVE The MLOps Engineer position will support various segments by enhancing and optimizing the deployment and operationalization of machine learning models. The primary objective is to collaborate with data scientists, data engineers, and business stakeholders to ensure efficient, scalable, and reliable ML model deployment and monitoring. The role involves integrating ML models into production systems, automating workflows, and maintaining robust CI/CD pipelines. RESPONSIBILITIES Model Deployment and Operationalization : Implement, manage, and optimize the deployment of machine learning models into production environments. CI/CD Pipelines: Develop and maintain continuous integration and continuous deployment pipelines to streamline the deployment process of ML models. Infrastructure Management: Design and manage scalable, reliable, and secure cloud infrastructure for ML workloads using platforms like AWS and Azure. Monitoring and Logging: Implement monitoring, logging, and alerting mechanisms to ensure the performance and reliability of deployed models. Automation: Automate ML workflows, including data preprocessing, model training, validation, and deployment using tools like Kubeflow, MLflow, and Airflow. Collaboration: Work closely with data scientists, data engineers, and business stakeholders to understand requirements and deliver solutions. Security and Compliance : Ensure that ML models and data workflows comply with security, privacy, and regulatory requirements. Performance Optimization : Optimize the performance of ML models and the underlying infrastructure for speed and cost-efficiency. EXPERIENCE Years of Experience: 4-6 years of experience in ML model deployment and operationalization. Technical Expertise : Proficiency in Python, Azure ML, AWS Sagemaker, and other ML tools and frameworks. Cloud Platforms: Extensive experience with cloud platforms such as AWS and Azure Cloud Platform. Containerization and Orchestration: Hands-on experience with Docker and Kubernetes for containerization and orchestration of ML workloads. EDUCATION/KNOWLEDGE Educational Qualification : Master's degree (preferably in Computer Science) or B.Tech / B.E. Domain Knowledge: Familiarity with EMEA business operations is a plus. OTHER IMPORTANT NOTES Flexible Shifts : Must be willing to work flexible shifts. Team Collaboration: Experience with team collaboration and cloud tools. Algorithm Building and Deployment : Proficiency in building and deploying algorithms using Azure/AWS platforms. Please share the following details along with the most updated resume to geeta.negi@compunnel.com if you are interested in the opportunity: Total Experience Relevant experience Current CTC Expected CTC Notice Period (Last working day if you are serving the notice period) Current Location SKILL 1 RATING OUT OF 5 SKILL 2 RATING OUT OF 5 SKILL 3 RATING OUT OF 5 (Mention the skill)