SRE MLOps Engineer

What we're looking for

Clearance Level Must Be Able to Obtain: Public Trust

Potential for Remote Work: Yes

Description We are seeking a versatile SRE/MLOps Engineer with DevSecOps expertise to design, automate, and operate secure, scalable, and repeatable model deployment workflows across the AI/ML Common Services environment. This role bridges infrastructure reliability, CI/CD automation, and model operations , enabling IRS mission teams to move from experimentation to production with confidence.

The engineer will not only support ML lifecycle operations (Databricks, MLflow, AWS SageMaker/Bedrock) but also bring DevSecOps rigor to ensure compliance, monitoring, and infrastructure-as-code are embedded in every step. By partnering with Infrastructure, Security, and Architecture teams, this role ensures the AAP environment is resilient, automated, and compliance-ready at enterprise scale.

• 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.

• Bachelor's or master's degree in computer science, Data Engineering, or a related technical discipline.
• 5+ years of experience in Site Reliability Engineering, DevOps, or MLOps with production-grade systems.
• Hands-on experience with Databricks, MLflow, or AWS SageMaker/Bedrock for ML model lifecycle operations.
• Strong proficiency in Terraform, CI/CD pipelines, and container orchestration (Docker, Kubernetes).
• Experience implementing security automation (e.g., IaC scanning, container security, SAST/DAST tools) within CI/CD workflows.
• Solid understanding of observability stacks (logs, metrics, tracing) and best operational practices.

Desired Skills:

• 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.

SAIC is a premier technology integrator providing full life cycle services and solutions in the technical, engineering, intelligence, and enterprise information technology markets. SAIC is Redefining Ingenuity through its deep customer and domain knowledge to enable the delivery of systems engineering and integration offerings for large, complex projects. SAIC's approximately 15,000 employees are driven by integrity and mission focus to serve customers in the U.S. federal government. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $4.5 billion. For more information, visit saic.com. For information on the benefits SAIC offers, see .