Software Development Engineer - ML Ops (US Federal)
Core
Design, implement, and deliver highly scalable features for the Machine Learning Runtime platform, partnering with Data Scientists and ML Engineers to power production ML features.
Role type
Senior Software Development Engineer (ML Ops / Infrastructure)
Builds
Microservices and infrastructure for Workday Machine Learning features in production
Domain
Enterprise Cloud, Machine Learning Infrastructure, US Federal Government
Deliverable
production ML models | infrastructure
Required skills
DevOps engineering, infrastructure automation, CI/CD pipeline development, Python programming, container orchestration (Docker, Kubernetes), distributed systems, Infrastructure as Code (Terraform), GitOps/CD engines, observability (Grafana, Prometheus), automated testing
Preferred skills
MLOps & Domain Experience, SaaS microservices architecture, Object-Oriented Design (OOD), technical leadership and mentoring
Technologies
Kubernetes, Docker, Python, Terraform, ArgoCD, Jenkins, Grafana, Prometheus, AWS, GCP
Responsibilities
Develop frameworks and automation tooling for efficiency; implement and operate distributed systems; deploy and orchestrate containers in production; research and prototype new ML tools; own features end-to-end including infrastructure as code; resolve operational issues and automate processes; provide on-call support
Seniority
Senior, hands-on IC
