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Mid-level ML role, hybrid/remote and generalist skillset attracts many qualified applicants.
Core ML engineering skills are transferable, but product and domain familiarity moderately matters.
Explicit 4–8 years plus specific MLOps, cloud, and tooling requirements yield strict candidate filters.
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Own end-to-end machine learning model development from data exploration, feature engineering to model building and validation for various supervised and unsupervised tasks.
Build and maintain production-ready ML pipelines and reusable components including training, deployment, monitoring, and CI/CD integration.
Manage ML model lifecycle practices such as experiment tracking, versioning, controlled rollout, and reproducibility to ensure scalable and robust production ML workflows.
4–8 years of experience in Data Science, ML Engineering, or Applied ML roles.
Strong hands-on experience in Python and SQL; production-level code conversion from notebooks mandatory.
Experience with ML lifecycle tools (e.g., MLflow, SageMaker), orchestration tools (Airflow, Kubeflow), and cloud platforms (AWS, Azure, GCP).
Familiarity with model deployment, monitoring (including data/model drift), containerization (Docker), version control (Git), and CI/CD automation explicitly required.
Experienced in bridging Data Science and Engineering to deliver production ML systems with business impact.
Comfortable with operational aspects of ML including multi-tenant config-driven pipelines and monitoring deployed models at scale.
Familiarity with NLP fundamentals, GenAI, and LLM workflows is a plus but not mandatory.