MLOps Engineer

Job Description

DPR is looking for an experienced Data and MLOps Engineer to join our Data and AI team and work closely with the Data Platform, BI and Enterprise architecture teams to influence the technical direction of DPR’s AI initiatives. You will work closely with cross‑functional teams, including business stakeholders, data engineers, and technical leads, to ensure alignment between business needs and data architecture and define data models for specific focus areas.

DPR is a leading construction company committed to delivering high‑quality, innovative projects. Our team integrates cutting‑edge technologies into the construction process to streamline operations, enhance decision‑making, and drive efficiency at all levels. We are looking for a MLOps Engineer to join our team and contribute to developing robust data solutions to support our Machine Learning, Data Science, Data Engineering and Software Engineering.

The MLOps Engineer will be instrumental in the design and implementation of scalable, cloud‑native solutions to meet the growing needs of our Data & Development team. The successful candidate will demonstrate the ability to abstract complexity and create reusable, scalable patterns that accelerate development. The MLOps Engineer will design, build and support the infrastructure and systems that enable our teams to deliver reliable, high‑impact data, workflows, and collaborating closely with data engineers, software developers, data scientists and product teams.

• Lead hands‑on implementation of automation‑first Dev Ops and MLOps practices, enabling infrastructure‑as‑code and consistent, repeatable environment provisioning
• Design and manage intelligent Data Ops pipelines with automated data quality monitoring and anomaly detection
• Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms
• Design and deploy containerized ML workloads, partnering with Infrastructure Engineering for cluster provisioning and governance
• Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows
• Implement AI‑driven data validation, schema drift detection, and metadata management
• Establish governance frameworks for AI systems, including bias detection, explainability, and auditability
• Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention
• Collaborate with Infrastructure Engineering to automate infrastructure provisioning
• Act as a technical point of contact for Dev Ops and MLOps practices, developing reusable patterns, documentation, and proof‑of‑concepts to drive adoption

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field
• 5+ years of experience in Dev Ops, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure
• Proficiency in at least one objected‑oriented programming language, preferably python with hands‑on experience in ml frameworks like Tensor Flow, PyTorch or Scikit‑learn

• Experience with CI/CD processes and automation
• Experience with Infrastructure as Code tools such as Terraform, Bicep
• Proficiency in containerized application deployments and container orchestration – experience with Kubernetes, especially AKS would be a huge plus
• Experience standing up and managing observability tools such as Datadog, Azure Monitor or Grafana for APM, LLM Ops and model performance monitoring
• Experience deploying production‑ready machine learning models
• Experience with Model explainability (SHAP, LIME) or similar
• Experience with cloud cost management and practices (e.g., Azure Cost Management, chargeback/show back models)

• Experience in Azure, particularly AKS, ACR, ARM, App Service, Azure Machine Learning and AI Foundry, Azure Monitor
• Familiarity with semantic search, retrieval‑augmented generation (RAG), or embedding pipelines
• Exposure to managing and…