MLOps Engineer

DPR Construction; North Carolina; On-site; USD 100,000 - 130,000; Full time

Job summary

DPR Construction in North Carolina is seeking an experienced MLOps Engineer to join our Data and AI team. You will be responsible for implementing infrastructure solutions, ensuring alignment between business needs and data architecture, and collaborating with cross-functional teams.

The ideal candidate will have a background in DevOps and MLOps, with a strong focus on cloud infrastructure and automation. This role provides an opportunity to influence our AI initiatives and work within a diverse team environment.

Qualifications

• 5+ years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering.
• Strong understanding of cloud infrastructure and experience with Azure.
• Experience with CI/CD processes and automation.

Responsibilities

• Lead hands-on implementation of DevOps and MLOps practices.
• Design and manage DataOps pipelines with automated data quality monitoring.
• Standardize observability practices across AI/ML and development teams.

Skills

CI/CD processes and automation

Infrastructure as Code tools

Containerized application deployments

Observability tools

Production-ready machine learning models

Education

Bachelor’s degree in Computer Science, Data Science, or related field

Tools

Terraform

Python

TensorFlow

Kubernetes

Datadog

Job description

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.

Position Overview

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.

Responsibilities

• Lead hands‑on implementation of automation‑first DevOps and MLOps practices, enabling infrastructure‑as‑code and consistent, repeatable environment provisioning
• Design and manage intelligent DataOps 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 DevOps and MLOps practices, developing reusable patterns, documentation, and proof‑of‑concepts to drive adoption

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field
• 5+ years of experience in DevOps, 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 TensorFlow, PyTorch or Scikit‑learn

Required Skills

• 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
• 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)

Nice to Have

• 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 monitoring ML workloads that support generative AI or advanced analytics use cases
• Proficiency with Snowflake
• Experience with workflow orchestration platforms such as Apache Airflow, Argo Workflow, Prefect, etc.

Equity Employment-Opportunity Statement

DPR is an equal opportunity employer and prohibits discrimination and harassment of any kind. All employment decisions at DPR are based on business needs, job requirements and individual qualifications, without regard to race, color, religion or belief, national or ethnic origin, sex (including pregnancy), age, physical or mental disability, HIV status, sexual orientation, gender identity and/or expression, marital, civil union or domestic partnership status, past or present military service, medical history or genetic information, or any other status protected by the laws or regulations in the locations where we operate. DPR will not tolerate discrimination or harassment based on any of these characteristics. Read more in our EEOE Policy.