Senior MLOps Engineer|Bangalore|6+Years

• We are seeking a highly skilled and experienced Senior MLOps Engineer to join our dynamic team in Bangalore. As a Senior MLOps Engineer, you will be responsible for designing, implementing, and maintaining robust machine learning operations frameworks. You will work closely with data scientists, software engineers, and other stakeholders to ensure seamless integration of machine learning models into production systems, enhancing scalability, reliability, and performance.

Required Qualifications:

• Experience: 6 to 10 years of experience in MLOps, DevOps, or related fields, with a strong background in deploying and managing machine learning models in production environments.
• Technical Skills: Proficiency in cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), CI/CD tools (Jenkins, GitLab CI, CircleCI), and orchestration frameworks (Airflow, Kubeflow).
• Programming Languages: Strong programming skills in Python, with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn) and scripting languages (Bash, Shell).
• Data Engineering: Experience with data pipelines, ETL processes, and big data technologies (Spark, Hadoop) is a plus.
• Problem-Solving: Excellent problem-solving skills, with the ability to troubleshoot complex ML deployment issues and optimize performance.
• Communication: Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders and collaborate effectively across teams.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• MLOps Strategy & Implementation: Develop and implement MLOps strategies to streamline the deployment, monitoring, and management of machine learning models in production.
• Infrastructure Management: Design and maintain scalable infrastructure for deploying machine learning models, leveraging cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Automation: Create automated workflows for model training, validation, deployment, and monitoring, ensuring continuous integration and continuous delivery (CI/CD) for ML models.
• Collaboration: Work closely with data scientists to understand model requirements and ensure proper integration with existing systems. Collaborate with DevOps teams to ensure robust and scalable deployments.
• Monitoring & Performance: Implement monitoring solutions to track model performance, detect anomalies, and manage model drift. Optimize model inference performance and resource utilization.
• Security & Compliance: Ensure the security and compliance of ML models and data pipelines, adhering to best practices and industry standards.
• Documentation & Training: Develop comprehensive documentation for MLOps processes and workflows. Provide training and support to team members on MLOps best practices and tools.