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Mid-level ML role in a metro city with broad skill requirements increases applicant competition.
ML and Azure-focused skills are transferable, but cloud and MLOps specifics raise domain sensitivity.
Explicit 4+ years requirement plus mandatory ML, Azure, and deep learning tooling increases filter strictness.
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Design, build, and evaluate scalable machine learning models for diverse use cases including classification, regression, forecasting, and NLP.
Develop and maintain data pipelines and deploy ML models using Microsoft Azure services like Azure ML, Azure Data Factory, and Azure Databricks.
Monitor model performance in production, apply AI/GenAI capabilities, and translate business needs into data science solutions while collaborating with cross-functional teams.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
4+ years of professional or project-based experience in data science or machine learning.
Strong proficiency in Python and hands-on experience with Microsoft Azure services including Azure ML, Databricks, Data Factory, and Synapse.
Experience with machine learning frameworks (scikit-learn, XGBoost, TensorFlow or PyTorch) and solid SQL skills.
Experienced with Azure ML Ops including experiment tracking (MLflow), model versioning, and CI/CD practices, indicating operational maturity.
Familiar with NLP libraries and generative AI technologies such as Azure OpenAI and LangChain, suggesting capability for advanced AI solution development.
Proficient in integrating cloud-native data engineering and BI tools (Azure Data Lake, Power BI), reflecting strong end-to-end ML workflow management.