Job ID 2511322
Location
REMOTE WORK, VA, US
Date Posted
2025-11-04
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.
Key Responsibilities
- 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
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.