Senior Data Platform Engineer – ModelOps

DigiKey is one of the fastest growing distributors of electronic components in the world. Since its founding in 1972, DigiKey has been committed to offering the broadest selection of in-stock electronic components, as well as providing the best service possible to its customers, aiding engineers through the entire design process, from Prototype to Production. This has led the company to be highly ranked year after year in industry surveys in North America as well as Europe and Asia, in categories covering such facets of business as availability of products, speed of service, responsiveness to problems, and more. 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 Qualifications - Bachelor’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 Ops - Strong Python + software engineering practices (testing, Git, modular code) - Experience deploying ML systems in batch and real-time environments - Hands-on with Docker + Kubernetes (AKS preferred) - Experience with CI/CD pipelines (Azure DevOps or GitHub Actions) - Experience implementing monitoring/alerting for production systems Collaboration & Support - Act as the primary platform contact for Model Ops. - Provide support and participate in incident response and root-cause analysis. - Mentor junior engineers to enable L1/L2 support and contribute to internal platform standards. Preferred Qualifications - Dataiku DSS (Automation node, API node, scenarios) - Azure services (ML, Storage, Key Vault, Monitor) - MLflow or model registry experience - LangChain / RAG / vector databases - Observability tools (Monte Carlo, Langsmith, Datadog or equivalents) All personal data collected will be used for recruitment purposes only. All unsuccessful applications will be deleted within 24 months.