Senior Data Platform Engineer ModelOps

Key Responsibilities

• Build and maintain end-to-end ML pipelines (train → validate → deploy → monitor)
• Productionize models for:
• Batch scoring (scheduled pipelines)
• Real-time APIs (Kubernetes-hosted services)
• Standardize deployments using:
• Dataiku (Automation + API nodes)
• Containerized services on Azure Kubernetes Service (AKS)
• Implement CI/CD pipelines (Azure DevOps/GitHub Actions) for ML workflows
• Establish monitoring and alerting:
• Model performance, drift, failures, latency
• Operationalize GenAI systems (LangChain/RAG):
• Prompt/version control, evaluation pipelines, tracing, cost controls
• Define and enforce model governance:
• Model registry, approvals, auditability, documentation
• Build reusable templates and paved roads for data scientists

Required Qualification

• sBachelor's degree in Computer Science, Engineering, or related field (16 years of formal education)
• .5 to 7 years of overall IT experience with 3+ years in Model Op
• sStrong Python + software engineering practices (testing, Git, modular code
• )Experience deploying ML systems in batch and real-time environment
• sHands-on with Docker + Kubernetes (AKS preferred
• )Experience with CI/CD pipelines (Azure DevOps or GitHub Actions
• )Experience implementing monitoring/alerting for production system

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Collaboration & Suppo

• rtAct as the primary platform contact for Model Op
• s.Provide support and participate in incident response and root-cause analysi
• s.Mentor junior engineers to enable L1/L2 support and contribute to internal platform standard

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Preferred Qualificati

• onsDataiku DSS (Automation node, API node, scenari
• os)Azure services (ML, Storage, Key Vault, Monit
• or)MLflow or model registry experie
• nceLangChain / RAG / vector databa
• sesObservability tools (Monte Carlo, Langsmith, Datadog or equivalen