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Tier-1 brand and Gurgaon location increase competition, but niche MLOps specialization moderates density.
Production-grade MLOps and Databricks focus moderately limits cross-industry transferability.
Explicit years plus mandatory Databricks, MLOps, Spark, and infra-as-code requirements make filters strict.
Lead design, development, and maintenance of MLOps capabilities including scalable model deployment pipelines and domain-specific model monitoring platforms.
Administer and optimize Databricks workspaces, infrastructure as code, and automated release pipelines to ensure reliable, secure, and scalable AI systems operation.
Mentor engineering team members and collaborate with data science, platform, and product teams to translate technical requirements and uphold operating standards.
Master’s degree with 3+ years or Bachelor’s degree with 5+ years in Computer Science, AI, Machine Learning, Data Science, Engineering, or related fields (or equivalent practical experience).
Hands-on experience with MLOps practices: model monitoring, feature catalogs, experiment tracking, model registry, and CI/CD pipelines.
Proven experience administering Databricks workspaces, including cluster/compute policies, job orchestration, upgrades, permissions, and secrets management.
Proficiency with Python, PySpark, SQL, CI/CD/build tools (Git, Jenkins, Maven, Artifactory), and deployment using infrastructure as code (e.g. Terraform, Ansible).
Senior-level AI engineering professional with strong MLOps and distributed data processing expertise (Spark and Databricks).
Experienced in building production-grade AI systems with emphasis on operational reliability, scalability, and security via automated pipelines and infrastructure as code.
Strong collaborator and mentor capable of enforcing engineering standards, guiding juniors, and working cross-functionally with data science and product teams.