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Metro location, popular MLOps title, and broad cloud/Databricks/containerization requirements drive high competition.
Role requests Automotive and B2B experience plus ML lifecycle expertise, making background fit highly sensitive.
Explicit 7+ years plus mandatory Databricks, cloud, containerization, and IaC skills create high shortlisting strictness.
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Design and manage data pipelines and engineering infrastructure for enterprise-scale machine learning systems, particularly in Automotive and B2B sectors.
Deploy, monitor, and maintain offline data scientist models into production ML systems using Databricks, ensuring auditability, versioning, and data security.
Evaluate and implement technologies to enhance performance, scalability, and reliability of production models; support CI/CD and automation practices for ML workflows.
7+ years in data analytics or business intelligence with at least 5+ years in model development, monitoring, and production.
Proven experience managing ML lifecycle including model deployment, monitoring, retraining, and scaling.
Strong expertise with cloud platforms (AWS/GCP/Azure), containerization (Docker, Kubernetes), and CI/CD tools (Jenkins, GitLab).
Bachelor’s degree in Information Management, Computer Science, Business Administration, or a related field.
Experienced in automotive and B2B industry contexts with demonstrated ability in ML infrastructure at scale.
Operates with strong software engineering rigor applying best practices like CI/CD and automation in ML production pipelines.
Skilled in cross-functional collaboration to translate business and technical requirements into scalable ML solutions using cloud and container orchestration technologies.