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Hybrid role, popular ML title, mid-level experience, metro location, broad MLOps skillset.
Core ML and MLOps skills are transferable across industries, though aftermarket domain knowledge increases specificity.
Explicit 4–8 years and multiple mandatory ML, MLOps, cloud, and deployment tools increase filter rigidity.
Translate business problems into ML problem statements and develop, validate, and improve ML models for various use cases including classification, regression, forecasting, and anomaly detection.
Build, maintain, and productize reusable ML pipelines covering data processing, feature engineering, model training, deployment, monitoring, and lifecycle management including versioning and rollout processes.
Collaborate with Engineering teams to integrate models into production workflows, ensuring production readiness, monitoring, and business impact measurement.
4–8 years of experience in Data Science, ML Engineering, Applied ML, or related roles.
Strong hands-on skills in Python, SQL, and production-quality code development beyond notebooks.
Experience with orchestration tools (Airflow, Kubeflow, Mage) and model lifecycle tools (MLflow, SageMaker, Azure ML, Vertex AI).
Experience with cloud platforms (AWS, Azure, or GCP), CI/CD automation tools (e.g. GitHub Actions), Docker, Git, and production ML monitoring concepts.
Experienced ML practitioner able to bridge data science experimentation and engineering for production ML workflows.
Comfortable with model evaluation, interpretability techniques (feature importance, SHAP), and analyzing model performance across segments and business outcomes.
Familiar with NLP fundamentals, GenAI/LLM use cases, and scalable multi-tenant/config-driven pipelines.