SRE MLOps Engineer

What you'll work on

• Enable secure, scalable, and repeatable deployment workflows for both ML models and supporting infrastructure.
• Build and maintain runtime environments, service accounts, orchestration logic for Databricks, MLflow, and AWS AI services.
• Implement and maintain CI/CD pipelines (Bitbucket, Bamboo, Jenkins, or equivalent) for code, data, and model deployments.
• Apply DevSecOps practices — integrating security scans, compliance checks, and audit logging into deployment pipelines.
• Collaborate with Infrastructure DSO and Solutions Architect to integrate Terraform-based IaC for consistent, automated provisioning.
• Implement observability, alerting, and logging (CloudWatch, Datadog, Prometheus) to monitor both application and ML workloads.
• Align infrastructure with ML lifecycle needs — including staging, promotion, rollback, retraining, and compliance-aware tracking.
• Develop automation templates, reusable workflows, and guardrails to accelerate onboarding of mission team models while ensuring security.
• Contribute to incident response, performance tuning, and reliability engineering across ML and non-ML workloads.

What we're looking for

• Active IRS clearance highly desired.
• Experience in federal or regulated environments with security, audit, and compliance requirements (FedRAMP, NIST 800-53).
• Knowledge of Trustworthy AI monitoring (bias detection, drift monitoring, explainability).
• Familiarity with Unity Catalog, Delta Lake, and data pipeline orchestration in Databricks.
• Hands-on experience with Zero Trust security models and secure boundary implementations.

• Relevant certifications such as

• Databricks Certified Machine Learning Professional.

• AWS DevOps Engineer – Professional.

• Certified Kubernetes Administrator (CKA).

• Security+ or equivalent security cert.

Target salary range $120,001 - $160,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.