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Remote hire and Bangalore location increase reach but senior niche MLOps requirements limit applicants.
Specialized MLOps and Databricks/cloud expertise reduces transferability across industries, so high sensitivity.
Extensive mandatory MLOps tools, Databricks, cloud and model deployment expertise imply high shortlisting strictness.
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Design, develop, and maintain scalable and reliable AI/ML Ops platforms and pipelines including model deployment and infrastructure management.
Implement CI/CD pipelines, automate retraining workflows, and monitor model performance, data drift, and latency post-deployment.
Manage version control & governance, optimize resource usage to minimize cloud costs while maintaining performance, and collaborate with cross-functional teams bridging development and production.
Experience building and maintaining AI/ML Ops platforms with scalability, reliability, efficiency, and security focus.
Hands-on experience with at least one major cloud provider (AWS preferred), Kubernetes, CI/CD, infrastructure-as-code tools (preferably Terraform), and observability/monitoring.
Proficiency in AI/ML frameworks and tools (e.g., MLFlow, Databricks Lakehouse, LangChain), programming in Python and SQL.
Legally eligible to work in India on an ongoing basis. Work Experience Required: Not explicitly mentioned in the JD.
Experienced in managing large-scale AI/ML systems handling petabytes of structured and unstructured data, particularly within Databricks ecosystems.
Capable of optimizing AI/ML operational costs through infrastructure/resource management and automation strategies.
Skilled in end-to-end AI/ML Ops lifecycle including deployment, monitoring, and security/compliance in cloud environments with solid cross-team collaboration experience.