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Mid-level ML/data role, metro locations, and broad skills create high applicant density.
Requires ML engineering and data pipeline expertise, moderately transferable across industries.
Explicit 4–8 years requirement plus mandated Spark, Databricks, and production ML experience.
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Design and develop scalable feature engineering and batch scoring pipelines for machine learning applications.
Deploy, monitor, and optimize machine learning models and inference services for real-time and batch predictions.
Build and maintain secure, scalable REST APIs for model serving and integrate ML services with upstream and downstream systems.
4–8 years of professional experience.
Strong proficiency in SQL and Python programming.
Hands-on experience with Apache Spark, Azure Databricks, and developing REST APIs using FastAPI or Flask.
Preferred notice period: Immediate to 30 days; Bachelor's or Master's degree in Computer Science, IT, Data Science, or related field.
Experience building and optimizing production-grade ML infrastructure and data pipelines at scale.
Familiarity with software engineering best practices including version control, testing, and code quality.
Ability to collaborate closely with Data Scientists, Backend Engineers, and Product teams to deliver end-to-end ML solutions.