General Dynamics Information Technology

AI/ML Technical Lead

Remote, US$153,000-$207,000Posted 2 months ago

Job Description

Clearance Level None Category Data Science and Data Engineering Location Remote, Working from the USA

Key Skills For Success Amazon Bedrock

Amazon Web Services (AWS)

Artificial Intelligence (AI)

Machine Learning (ML)

Retrieval-Augmented Generation

##### REQ#:RQ219814

##### Public Trust:BI Full 6C (T4)

##### Requisition Type:Regular

##### Your Impact

Own your opportunity to be on the frontlines of health innovation. Deliver for America’s health agency missions and enhance lives for hundreds of millions every day.

Job Description

-------------------

Own your opportunity to turn data into measurable outcomes for our customers’ most complex challenges. As an Artificial Intelligence (AI)/Machine Learning (ML) Technical Lead at GDIT, you’ll power in joining our team to support the Centers for Medicare and Medicaid Services (CMS). Work Visa sponsorship will not be provided.

At GDIT, people are our differentiator. As an AI/ML Technical Lead supporting CMS, you will architect, develop, integrate, and operationalize AI/ML Technical Lead capabilities that enhance decision support and operational readiness across environments. You will be responsible for designing, developing and deploying AI models, algorithms and systems to enhance productivity, decision-making and automation within the organization.

Key Responsibilities

  • Serve as the technical lead for AI/ML strategy, architecture, and implementation across the program.
  • Design and deploy scalable AI/ML models in support of the CMS mission.
  • Lead development of predictive analytics, automation frameworks, and intelligent decision‑support systems.
  • Integrate AI solutions into secure enterprise and tactical environments.
  • Ensure compliance with CMS cybersecurity and data governance standards.
  • Provide oversight of data engineering pipelines and model lifecycle management (MLOps).
  • Develop and maintain model validation, explainability, and bias mit

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