Senior AI ML Engineer at SAIC
Washington, DC 20005, USA -
Full Time


Start Date

Immediate

Expiry Date

10 Sep, 25

Salary

200000.0

Posted On

10 Jun, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Computer Science, Communication Skills, Version Control, Model Development, Irs, Sql, Spark, Infrastructure, Devops, Code, Cross Team Collaboration, Federal Agencies, Python

Industry

Information Technology/IT

Description

Job ID: 2506488
Location: REMOTE WORK, DC, US
Date Posted: 2025-06-09
Category: Information Technology
Subcategory: Platform Engr
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
SAIC are seeking a seasoned AI/ML Engineer to join the IRS’s flagship Analytics Application Platform (AAP) — a mission-critical Platform-as-a-Service (PaaS) that enables secure, compliant, and scalable AI/ML workloads across the agency.
AAP empowers mission teams to develop and operationalize both traditional and GenAI models through a unified environment that integrates Databricks, JupyterHub, AWS SageMaker, Bedrock, and other core services. As an AI/ML Engineer, you will play a central role in building reusable patterns, advancing infrastructure readiness, and ensuring all platform services meet the technical, compliance, and performance standards required for IRS-wide production use.

REQUIRED QUALIFICATIONS:

  • Bachelor’s or master’s degree in computer science, Engineering, or a related technical field. Equivalent practical experience in AI/ML platform engineering will also be considered.
  • 7+ years of experience in AI/ML engineering, with significant hands-on experience using Databricks, JupyterHub, and AWS AI/ML services (e.g., SageMaker, Bedrock, Lambda, RDS, S3).
  • Proficient in Python, SQL, Spark, and machine learning frameworks used in both model development and deployment.
  • Demonstrated experience with MLFlow, CI/CD pipelines, infrastructure-as-code, and Git-based version control.
  • Deep familiarity with building secure, compliant ML infrastructure in a regulated federal environment.
  • Strong communication skills with the ability to collaborate across platform, customer success, and security teams.

DESIRED SKILLS:

  • Prior experience supporting AI/ML in regulated environments such as IRS, Treasury, or other federal agencies.
  • Familiarity with Immuta, Trustworthy AI controls, and model auditability frameworks.
  • AWS certification or similar credentials in cloud engineering or DevOps.
  • Exposure to customer onboarding, cross-team collaboration, and platform-as-a-service delivery models.
    Target salary range: $160,001 - $200,000. The estimate displayed represents the typical salary range for this position based on experience and other factors
Responsibilities
  • Build and maintain robust pipelines for data ingestion, exploration, feature engineering, and model training across Databricks and AWS-native services.
  • Operationalize tools such as MLFlow for experiment tracking, model registry, versioning, and lifecycle governance.
  • Integrate with T-Cloud Bitbucket and CI/CD pipelines (e.g., Bamboo) to enable secure, traceable development workflows.
  • Configure and manage IRS-compliant AWS S3 feature stores, ensuring UNAX-compliant isolation and secure access per team.
  • Develop and orchestrate AWS services (Lambda, RDS, SNS, EventBridge) to support automated model promotion and metadata tracking.
  • Support GenAI enablement by engineering reusable, secure pathways for customers to leverage AWS Bedrock and SageMaker for LLM-based applications.
  • Collaborate across the Infrastructure, Customer Success (CSx), and ATO/OneSDLC teams to support onboarding and productionalization for IRS customer use cases.
  • Implement and maintain Responsible AI tooling, including the Responsible AI Toolbox, to support auditability and ethical deployment practices.
  • Produce high-quality documentation and reusable code artifacts that simplify customer experience and platform adoption.
  • Support platform-wide upgrades (e.g., Databricks E2), refactoring of inherited IaC, and improvements in CI/CD automation and monitoring.
  • Actively contributes to platform maturity by helping define repeatable patterns for scaling ML/GenAI use cases with speed and trust.
    Qualifications
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