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AI Systems Engineer

Job in Chantilly, Fairfax County, Virginia, 22021, USA
Listing for: KBR Careers
Full Time position
Listed on 2026-10-05
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 152600 - 190700 USD Yearly USD 152600.00 190700.00 YEAR
Job Description & How to Apply Below
Title:

AI Systems Engineer

Title:

AI Systems Engineer K  is seeking a visionary AI Systems Engineer to join our team, providing critical technical services to the U.S. Government (USG). This role supports a dedicated USG Program Office, in shaping, designing, and deploying cutting‑edge AI and Machine Learning (AI/ML) initiatives for national security space architectures. Position Overview As an AI Systems Engineer, you will serve as both a hands‑on technical architect and a strategic advisor.

You will leverage the Government customer’s massive enterprise datasets, spanning complex program budgets, scheduling, and financials, to build proof‑of‑concept models (including Large Language Models and financial predictive tools). You will lead the evolution of the AI roadmap by identifying cutting‑edge commercial technologies and directing successful prototypes into robust, production‑ready enterprise tools.

Responsibilities
  • Strategic LLM Initiatives:
    Help shape and define the AI/ML roadmap for the Government customer office. Guide the government on how to securely build, adopt, govern, and maintain foundation models and custom LLMs across sensitive environments, ensuring robust model lifecycle management.
  • Rapid Prototyping:
    Design and build hands‑on, high‑impact proof‑of‑concept models. This includes training, fine‑tuning, and leveraging Large Language Models (LLMs) for search, summarization, and business intelligence, as well as developing advanced financial, budget, and scheduling analysis models.
  • Data Exploitation:
    Work directly with massive, unstructured and structured enterprise datasets (financials, acquisition timelines, schedules, program execution metrics). Clean, curate, and pipeline this data to create high‑quality training corpora, instruction‑tuning datasets, and knowledge bases for Retrieval‑Augmented Generation (RAG) to discover trends, anomalies, and optimization opportunities.
  • Technology Curation & Scouting:
    Actively scout the commercial tech sector, academia, and open‑source communities for breakthroughs in generative AI, NLP, and predictive analytics. Curate a pipeline of high‑performing external groups and technologies for USG consideration.
  • Contractor & Vendor Management:
    Formulate technical recommendations, draft statements of work, and establish the system integration requirements needed to bring external contractors on board to scale and operationalize your prototypes.
  • Technical SETA Advisory:
    Provide objective technical evaluation of contractor proposals, designs, and deliverables to ensure they align with government AI safety, security, and performance standards. This includes overseeing LLM red‑teaming, bias testing, hallucination mitigation, and ongoing alignment evaluations.
Work Environment
  • Location:

    On‑Site (Chantilly, VA area)
  • Travel Requirements:
    Minimal (0-20%)
  • Working Hours:

    Standard
Required Qualifications
  • Clearance:
    Active TS/SCI with Polygraph.
  • Education & Experience:

    Bachelor’s degree with 5+ years of relevant experience, Master’s degree with 3+ years of relevant experience, or PhD with 0+ years of relevant experience.
  • Technical

    Skills:

    Strong foundations in AI/ML concepts, natural language processing (NLP), and statistical modeling.
  • Prototyping

    Experience:

    Demonstrated experience building prototypes using Python, R, or similar tools, specifically with frameworks related to LLMs (e.g., llama.cpp, Lang Chain, Hugging Face) or data analytics.
  • Analytical

    Skills:

    Experience working with complex enterprise datasets (e.g., financial, resource planning, schedule networks, or programmatic data).
  • Communication:
    Exceptional ability to translate highly technical AI concepts—such as LLM training methodologies, RAG architecture, model alignment, and corpus curation—into strategic,…
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