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Tier-1 employer, Bangalore location, and mid-level (4–6yr) MLOps role increase candidate competition.
Requires specialized MLOps, GPU, and Databricks experience but skills are moderately transferable across industries.
Explicit 4–6 years plus mandatory MLOps, GPU, Databricks, CI/CD, and IaC requirements tighten shortlisting.
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Design, develop, and maintain a hybrid AI platform including MLOps and LLMOps pipelines across Databricks and on-prem clusters.
Ensure scalability, reliability, and efficiency of AI and data pipelines with monitoring, root cause analysis, and corrective action implementation.
Collaborate with data scientists, engineers, and architecture teams to integrate AI operations solutions aligning with security, risk, and operational SLAs.
4-6 years of experience in AI Ops and ML Ops engineering or operations with a proven record of delivering complex AI projects.
Proficiency with Databricks, CICD (such as Azure DevOps), ML frameworks (TensorFlow, PyTorch), MLOps tools (MLflow, Kubeflow), cloud platforms (Azure, AWS), and infrastructure as code (Terraform).
Hands-on experience with GPU computing and AI workload optimization; programming skills in Python, Java, or C++.
Education: Engineering degree or equivalent; certifications in AI/ML, Databricks, Azure, Terraform are noted but not mandatory filters.
Experienced in managing scalable AI platforms in hybrid environments involving both cloud and on-prem infrastructure.
Comfortable working in cross-functional teams with strong collaboration between data science, engineering, and governance.
Has a strong technical background with focus on operational excellence, system monitoring, and code quality to minimize technical debt and maintain robust deployable pipelines.