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Tier-1 brand, metro location, and mid-level generalist ML role increase applicant competition.
MLOps and production ML platform skills are transferable across industries but require specialized ML experience.
Multiple mandatory MLOps tooling, cloud, and production governance requirements enforce strict technical screening.
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Manage end-to-end ML model lifecycle including versioning, deployment, rollback, and A/B testing frameworks in collaboration with ML engineers.
Develop and maintain CI/CD and orchestration workflows using tools like GitLab CI, Airflow, and Argo Workflows for model deployment and automation.
Monitor production ML models for data drift, performance degradation and ensure governance, security, compliance including audit logs and model reproducibility.
Bachelor's degree or higher in Computer Science, Software Engineering or related field (or equivalent experience).
Hands-on experience designing and implementing cloud-based ML workflows on AWS.
Proficiency with MLOps tools like MLflow, Kubeflow, Airflow, and container orchestration with Docker, Kubernetes (EKS/GKE/AKS).
Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, scikit-learn.
Experienced in building scalable ML platforms with strong software engineering rigor including version control, testing, automation, and CI/CD pipelines.
Comfortable operating in cross-functional agile teams collaborating with data scientists, data engineers, and architects.
Proficient in ML model governance, monitoring, and orchestration on cloud platforms using infrastructure as code and observability tools.