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Strong employer brand, popular ML role, and metro location increase candidate competition.
Core ML and MLOps skills transfer across industries, so candidates from diverse sectors fit well.
Explicit 8-12 years plus mandatory MLOps, cloud, and tooling requirements make filters highly strict.
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Develop, train, evaluate, and deploy machine learning models collaborating with data scientists and product teams.
Build, maintain, and scale MLOps pipelines including data ingestion, feature engineering, deployment, and model monitoring.
Leverage cloud platforms (AWS, GCP, Azure) and implement DevOps/MLOps best practices to automate and optimize ML workflows, including conducting A/B testing.
8-12 years of experience in Computer Science, IT or related field.
Proficiency in machine learning algorithms, Python, and ML libraries (TensorFlow, PyTorch, Scikit-learn).
Experience with MLOps tools (MLflow, Kubeflow, Airflow) and DevOps tools (Docker, Kubernetes, CI/CD).
Experience with cloud platforms like AWS, GCP, or Azure.
Strong expertise in end-to-end machine learning lifecycle and MLOps pipeline development in cloud environments.
Experience working cross-functionally with data scientists, engineers, and product teams to deliver ML solutions.
Demonstrated ability in automating ML workflows and monitoring model performance for scalable production deployment.