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Medium — metro roles, known analytics brand, mid-level experience but niche MLOps skillset.
Medium — MLOps skills transfer across industries but require substantial ML and production engineering background.
High — explicit 5–10 years plus mandatory MLOps tools, cloud, CI/CD and production ML experience.
Collaborate with Data Scientists and Data Engineers to deploy, operate, and automate machine learning models in production environments.
Build and maintain scalable ML pipelines and MLOps components using tools like MLFlow, Kubeflow, or equivalent platforms.
Develop CI/CD components, enable model tracking and experimentation, and support troubleshooting across development, testing, and production phases.
5-10 years of experience in production-quality software development.
Strong experience in System Integration, Application Development or Data Warehouse projects in enterprise environments.
Proficiency in object-oriented languages such as Python, PySpark, Java, C#, or C++ and database programming with SQL.
Basic knowledge of MLOps, machine learning, Docker, CI/CD pipelines, Git, and foundational cloud computing (AWS, Azure, or GCP).
Minimum qualification: B.E/B.Tech/M.Tech in Computer Science or related technical degree or equivalent.
Experienced in managing end-to-end machine learning model deployment and operations in production settings.
Comfortable working across the complete ML development lifecycle with cloud and on-premise infrastructure.
Able to handle technical collaboration, team leadership, problem-solving, and client engagement in a fast-paced, multi-domain environment.