MLOps Engineer

Role Overview

DPR is seeking an experienced Data and MLOps Engineer to join its Data and AI team. You will work closely with Data Platform, BI, and Enterprise Architecture teams to influence the technical direction of DPR’s AI initiatives—translating business needs into data architecture and reusable, scalable patterns.

Responsibilities

• Lead hands-on automation-first DevOps and MLOps practices, including Infrastructure-as-Code and consistent environment provisioning.
• Design and manage DataOps pipelines with automated data quality monitoring and anomaly detection.
• Standardize observability across AI/ML and development teams (logging, metrics, tracing, and model performance monitoring).
• Design, build, and support containerized ML workloads, partnering with Infrastructure Engineering on cluster provisioning and governance.
• Extend CI/CD pipelines to automate infrastructure changes and ML workflows.
• Implement AI-driven data validation, schema drift detection, and metadata management.
• Establish AI governance frameworks including bias detection, explainability, and auditability.
• Automate Azure RBAC (role and permission management) to reduce manual work.
• Collaborate with Infrastructure Engineering to automate infrastructure provisioning.
• Serve as a technical point of contact for DevOps/MLOps practices, producing patterns, documentation, and proof-of-concepts.

Requirements

• Bachelor’s degree in CS, Data Science, Information Systems, or related field.
• 5+ years experience in DevOps/MLOps/Data Engineering/Software Engineering/SRE.
• Strong understanding of cloud infrastructure, preferably with Azure.
• Proficiency in an object-oriented language—Python preferred—with hands-on experience in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

Required Skills

• Experience with CI/CD and automation.
• Infrastructure-as-Code (e.g., Terraform, Bicep).
• Containerized deployments and orchestration—Kubernetes (especially AKS) is a plus.
• Observability tooling experience (e.g., Datadog, Azure Monitor, Grafana) for APM and model performance monitoring.
• Experience deploying production-ready ML models.
• Model explainability experience (e.g., SHAP, LIME).
• Cloud cost management (e.g., Azure Cost Management, chargeback/showback).

Nice to Have

• Additional Azure experience, particularly AKS.