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Mid-level niche MLOps role in metro locations at a known analytics firm yields moderate applicant competition.
Requires specialized MLOps and ML production experience, limiting transferability across non-ML roles.
Explicit 5-10 years plus mandatory MLOps, cloud, and CI/CD experience enforces high filtering.
Deploy and operate advanced analytics machine learning models by collaborating with Data Scientists and Data Engineers.
Automate and streamline model development and operations, including building and maintaining deployment, monitoring, and operational tools.
Develop scalable ML pipelines and MLOps components using platforms like MLFlow, Kubeflow, DataRobot, and manage end-to-end ML lifecycle on Cloud (AWS, Azure, GCP) or On-Prem environments.
5-10 years experience building production-quality software.
Strong experience in system integration, application development, or data warehouse projects with enterprise technologies.
Basic knowledge of MLOps, machine learning, Docker, and foundational cloud computing skills in AWS, Azure, or GCP.
Proficiency in object-oriented programming languages (e.g., Python, PySpark, Java, C#, C++), SQL database programming, experience with CI/CD for ML pipelines, and Git for source code management.
Experienced in full ML development lifecycle and MLOps, capable of handling complex challenges in fast-paced environments.
Skilled in client-facing roles including business development and delivery across multiple domains, showcasing project management and team handling capabilities.
Comfortable working with cloud and on-prem ML infrastructure, demonstrates ability to build scalable, automated ML solutions using modern MLOps platforms.