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InterImage

AI Program Analyst

Arlington, Virginia, United StatesUnited States
Full-time
POSTED 8 SEPTVERIFIED 6H AGO
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Role
64 days opentrue age
Posted 8 Sept
Pay
$55k–$140kno pay posted
Market p25–p75
$55k–$140k
United States · n=35463 · 365d to 11 Nov
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LLMs

PixelRain AI is a small, fast-moving AI company delivering an AI-powered product-lifecycle knowledge platform for a U.S. Department of War (DoW) customer: governed ingestion of technical documents, an enterprise ontology and knowledge graph, and generative-AI applications that draft maintenance-planning products and answer engineering questions with citations — running in the customer's cloud environment, with an offline variant for disconnected use. Small team, real users, working software demonstrated every sprint.

Salary: 110,000 - 140,000+

Active Secret Clearance Required

This role is the connective tissue of the program: part business analyst, part tester, part program coordinator. You will keep delivery organized and be trusted with real program-management responsibilities as you grow into them.

What you'll do

  • Prepare and submit the monthly progress and funds-status reports to the customer.
  • Track assignments and action items across the team; keep sprints, milestones, and deliverables organized and moving.
  • Support requirements: capture and document customer needs, write acceptance criteria, and keep them current.
  • Plan and run testing: test cases, execution, defect tracking, and test reports against acceptance criteria.
  • Prototype and validate with AI tools — build small utilities, test harnesses, and demos using AI-assisted development.

Requirements

  • 4+ years as a business analyst, tester, or program analyst on software programs.
  • Experience writing and submitting customer-facing status reports and tracking deliverables and actions.
  • Hands-on requirements and testing experience — you’ve written test cases and run them, not just coordinated them.
  • Hands-on AI-assisted development experience (“vibe coding”) and working familiarity with LLMs.
  • Familiarity with retrieval-augmented generation (RAG) strongly preferred; knowledge-graph exposure ideal.
  • Organized and self-directed — comfortable wearing several hats on a small team.
  • U.S. citizenship required; must be able to obtain a Common Access Card (CAC).
Source · InterImageFirst seen 11 NovOriginal ↗