Principal Machine Learning Ops Engineer

If you’re passionate about building a better future for individuals, communities, and our country—and you’re committed to working hard to play your part in building that future—consider WGU as the next step in your career.

Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families.

The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.

At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:
 

Grade: Technical 413

Pay Range: $197,000.00 - $305,300.00

Job Description

Job Summary

The Principal Machine Learning Operations Engineer (Principal MLOps Engineer) is an action-oriented position that designs and builds automated processes. These processes focus on machine learning (ML) service and infrastructure stability, which enables our digital Educational Technology (Ed Tech) transformation product to use advanced NLP, knowledge engineering, and ML to accelerate innovation in scientific operations. The ideal candidate will have cloud infrastructure, IAC, and monitoring/instrumentation skills. A proven track record of collaboration, iteratively implementing data-intensive solutions, and strong project leadership are also required to be successful in this role. The Principal MLOps Engineer will educate stakeholders, mentor team members and, with a strong vision for how the ML/SE discipline can proactively create positive impacts, have a significant stake in defining the future of the Ed Tech function for WGU.

Job Responsibilities

Platform Ownership & Development

• Architect, build, and maintain ML infrastructure leveraging Databricks and AWS.

• Lead the development of reusable MLOps tooling, SDKs, CI/CD templates, and pipelines.

• Implement and extend Databricks Asset Bundles, Databricks APIs, and Agent Frameworks for model and GenAI workloads.

CI/CD & Automation

• Design and implement automated CI/CD pipelines using tools like GitHub Actions, Infrastructure-as-Code (Terraform, AWS CDK), and templating frameworks (Copier, Cookiecutter).

• Automate testing, deployment, and rollback workflows to streamline ML lifecycle from experimentation to production.

Model Lifecycle Management

• Manage the ML lifecycle using MLflow for both classic ML (experiments, registry) and GenAI use cases (traces, evaluations).

• Collaborate with data scientists to deploy, monitor, and iterate on ML models in real-time and batch inference environments.

• Develop scalable and low-latency online inference systems, exposed via APIs or service endpoints.

Monitoring, Observability & Performance

• Implement monitoring solutions for drift detection, data quality, throughput, latency, and model performance using tools like Evidently and custom dashboards.

• Proactively manage model and system reliability, scalability, and resiliency in production environments.

GenAI & Agentic Workflows

• Design and deploy agentic workflows using frameworks like LangChain, and integrate with Databricks Agent Frameworks.

• Collaborate with ML/GenAI teams to evaluate and productionize LLM-based applications and tools.

Minimum Qualifications

• 7+ years of software engineering or DevOps experience, with at least 4 years focused on MLOps or ML infrastructure.

• Deep experience with Python, SQL, and Databricks platform development.

• Strong proficiency in deploying and managing ML systems on AWS, including EC2, S3, EKS, SageMaker, or Lambda.

• Hands-on experience with MLflow, GitHub Actions, Databricks SDKs, and infrastructure as code tools (Terraform, CDK, etc.).

• Experience build