MLOps Engineer

About the Role

We are seeking skilled MLOps / DevOps / CloudOps Engineers with 2 to 9 years of experience to join our team. The role involves designing, automating, deploying, and sustaining scalable cloud-native applications alongside machine learning platforms. Candidates should bring proficiency in cloud technologies, continuous integration and delivery pipelines, infrastructure automation, containerization, and monitoring to ensure operational excellence across multiple cloud providers.

Key Responsibilities

• Develop and maintain comprehensive machine learning pipelines covering model creation, deployment, monitoring, and re-training cycles.
• Utilize MLOps best practices to automate ML workflows effectively.
• Manage production deployment of machine learning models and ensure their operational stability.
• Partner with Data Scientists, Data Engineers, and platform teams for seamless collaboration.
• Implement mechanisms for monitoring models, tracking their performance, and detecting drift.
• Design, implement, and support CI/CD pipelines to streamline software deployments.
• Automate provisioning of infrastructure and oversee configuration management.
• Oversee version control including code repositories, branching strategies, and release management.
• Incorporate DevSecOps principles to enforce security in deployment pipelines.
• Enhance deployment processes for increased efficiency, reliability, and scalability.
• Administer and optimize cloud infrastructures across providers such as AWS, Azure, and Google Cloud Platform.
• Continuously monitor cloud-based resources regarding performance, costs, and availability.
• Troubleshoot and resolve issues related to infrastructure and applications.
• Guarantee high availability, disaster recovery planning, and business continuity strategies.
• Apply infrastructure-as-code approaches and ensure compliance with cloud governance standards.

Eligibility Criteria

Applicants must hold a B.Tech or B.E. degree in any specialization.