Security-Cleared AI Engineer for Federal Analytics

Accenture; Arlington (VA); On-site; USD 102,000 - 170,000; Full time

Job summary

Accenture Federal Services in the DC region is seeking a hands-on ML/AI engineer to shape the next generation of mission-critical analytics. You will design and productionize AI agents that reason through complex scenarios in a secure government cloud, collaborating with federal clients across defense, health, and civilian agencies.

This role focuses on prompt engineering, multi-agent simulations, and cost-aware scaling of large language models, with emphasis on reliability and SME feedback.

Qualifications

• US Citizen
• Active Secret clearance
• Bachelor’s degree in computer science, data science, or a related field or equivalent practical experience (e.g., 4 extra years of hands-on work)
• Experience in applied ML/AI engineering, including production prompt engineering or agentic system design
• Hands-on experience with large language model APIs and scaling for cost and performance
• Proficiency with Git or other version control tools
• Ability to work full-time on-site in the DC area

Responsibilities

• Develop, test, and refine prompts for AI agents in multi-agent simulations
• Integrate with a cloud-based generative AI platform, optimizing for performance and cost
• Validate AI outputs using statistical models and SME feedback
• Enhance agent reliability and realism as new use cases emerge

Skills

Prompt engineering

LLM integration

Agent design

Cloud platform familiarity

Cost optimization

Education

Bachelor's degree in computer science, data science, or related field

Tools

Git

Palantir Foundry

Palantir AIP

Job description

Accenture Federal Services in the DC region is seeking a hands-on ML/AI engineer to shape the next generation of mission-critical analytics. You will design and productionize AI agents that reason through complex scenarios in a secure government cloud, collaborating with federal clients across defense, health, and civilian agencies.

This role focuses on prompt engineering, multi-agent simulations, and cost-aware scaling of large language models, with emphasis on reliability and SME feedback.