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Mid-level MLOps role, metro locations, common skills, and broad applicant pool increase competition.
MLOps skills are transferable across industries but require ML domain experience, yielding moderate sensitivity.
Explicit 3–5 years requirement plus mandatory MLOps tools, CI/CD, and cloud skills tighten candidate filters.
Develop, deploy, and maintain scalable ML pipelines and MLOps components across the machine learning development lifecycle using tools like MLFlow, Kubeflow, and cloud platforms (AWS, Azure, GCP).
Collaborate with Data Scientists and Data Engineers to automate model tracking, experimentation, deployment, and monitoring for production-level ML systems.
Troubleshoot, resolve issues in development, testing, and production environments and support client delivery engagements involving advanced AI solutions.
3 to 5 years of experience building production-quality software with strong system integration or application development background.
Proficiency in object-oriented programming languages such as Python, PySpark, Java, C#, or C++.
Experience developing CI/CD pipelines for production-ready ML solutions and knowledge of SQL and Git.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field.
Experienced in designing and managing end-to-end production ML pipelines with familiarity in MLOps tools and cloud platforms.
Comfortable working in fast-paced environments with cross-functional teams including data scientists and engineers to deliver AI-driven solutions.
Has prior exposure to handling client engagements and technical project management related to AI and machine learning operations.