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Mid-level ML/Data role, popular title, metro locations, and known employer increase applicant competition.
Skills transferable across industries but require ML-specific experience and tooling, so moderate transferability.
Explicit 4–8 years requirement plus mandatory Spark, Databricks, Python, and model deployment skills enforce strict filters.
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Design and develop scalable feature engineering and batch scoring pipelines for large-scale ML workloads.
Develop, deploy, and maintain real-time and batch model inference services and secure REST APIs for model serving and data integration.
Collaborate with cross-functional teams to deliver end-to-end cloud-native ML solutions ensuring performance, scalability, and reliability.
4–8 years of work experience in data engineering or ML engineering roles.
Strong proficiency in SQL, Python programming, and experience with Apache Spark and Azure Databricks.
Experience building and deploying machine learning models in production and developing REST APIs using frameworks like FastAPI or Flask.
Bachelor's or Master's degree in Computer Science, IT, Data Science, or a related field.
Experienced in designing and optimizing scalable data pipelines and ML infrastructure in production environments.
Proficient in integrating ML services with other systems and managing API security, scalability, and performance.
Versed in Agile software development and cloud platforms such as Azure, AWS, or GCP, with strong analytical and troubleshooting skills.