Job ID: 2508971
Location: REMOTE WORK, VA, US
Date Posted: 2025-08-21
Category: Engineering and Sciences
Subcategory: Solutions Archt
Schedule: Full-time
Shift: Day Job
Travel: No
Minimum Clearance Required: None
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.

REQUIRED QUALIFICATIONS:

• 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.
• Must be a U.S. Citizen with the ability to obtain and maintain a Public Trust security clearance.
• 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

• 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.
  Qualifications