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Medium: strong global brand and popular ML title but senior level and specific PySpark requirement limit applicant pool.
Low: core ML deployment and pipeline skills are broadly transferable across industries.
High: senior role with mandatory PySpark, Azure, containerization, CI/CD and observability requirements.
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Own backend engineering strategy for ML platform focusing on scalable integration with software applications and visualization layers.
Lead full deployment lifecycle of ML models including training, deployment, and monitoring on devices.
Develop and maintain scalable real-time ML pipelines and establish CI/CD pipelines for continuous model integration and deployment.
Strong experience with PySpark (deep understanding required).
Experience with containerization technologies like Docker and Azure Container Registry (ACR).
Experience managing GitHub repositories with structured, well-documented, modularized code.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in backend ML engineering with ownership of end-to-end model deployment and monitoring.
Proficient in building real-time analytics pipelines supporting experimentation across business functions.
Skilled at architecting and maintaining CI/CD pipelines, observability layers, and ensuring security and governance compliance in centralized data environments.