Job Description

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.

Key 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

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.

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