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Tier-1 brand and Bengaluru metro increase competition, but senior specialized MLOps requirements narrow the pool.
Requires deep MLOps, Databricks, LLM, and enterprise cloud experience, limiting cross-industry transferability.
Explicit 12+ years plus specific Databricks, AWS, Kubernetes, Terraform, and security requirements imply high strictness.
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Design and operate scalable, secure MLOps infrastructure across AWS and Databricks to deploy machine learning models and large language models at scale.
Build and maintain automated CI/CD pipelines, monitoring, rollback, and recovery systems for continuous model deployment using cloud-native and container technologies.
Collaborate with cross-functional teams to establish production-readiness standards, observability, security, and governance for AI-powered services.
Experience: 12+ years in MLOps, platform engineering, or production machine learning with demonstrated deployment and operation of ML models in production.
Technical skills: Expertise with AWS services, Databricks (including MLflow and model serving), Kubernetes (EKS/ECS), Python, containerization (Docker), Infrastructure as Code (Terraform or CloudFormation), and CI/CD pipelines.
Education: Bachelor’s or Master’s in Computer Science, Software Engineering, Data Engineering, Machine Learning, or related field, or equivalent experience.
Work Experience Required: Explicitly 12+ years relevant experience mentioned in the JD.
Experienced platform engineer balancing cloud architecture, infrastructure automation, CI/CD, and incident management with deep understanding of ML system operational risks and deployment constraints.
Practitioner familiar with deploying LLM-based AI applications and observability tools, able to translate experimental AI solutions into secure, reliable, scalable production services.
Comfortable working across multi-disciplinary teams to implement security, compliance, and cost governance in regulated or enterprise environments.