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AI Engineer – Agentic Orchestration

Job in Fredericksburg, Stafford County, Virginia, 22407, USA
Listing for: JFL Consulting LLC
Full Time position
Listed on 2026-10-03
Job specializations:
  • Software Development
    Backend Developer, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 160000 - 190000 USD Yearly USD 160000.00 190000.00 YEAR
Job Description & How to Apply Below
About this position

Description:

Job Title: AI Engineer – Agentic Orchestration (Senior)

Place of Performance: DC-Metro Area Preferred

Mandatory Requirements: Secret clearance

Experience Level: 6-8+ years

On-Site, hybrid, remote: Remote (Occasional customer on-site meetings)

Travel: Minimal / As Required

About JFL Consulting: With more than 20 years of securing some of the U.S. Department of Defense and the Intelligence Community’s most critical networks, JFL Consulting, LLC provides advanced network security solutions to a range of US Government and US commercial clients. Our cybersecurity operators are experts at assessing and defending mission-critical data and the networks that facilitate their operation. We are focused on delivering advanced products and industry best practices that meet each customer’s unique requirements.

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What We Offer
  • Salary: $160k - $190k
  • 100% employer-paid medical, dental, and vision premiums for employees and dependents
  • Flexible Spending Accounts (healthcare, dependent care, and commuter)
  • Life insurance, short-term disability and long-term disability
  • 401(k) with immediate vesting of company contribution
  • Generous PTO policy (15 vacation, 5 sick, 2 personal days, 11 holidays)
  • We support your growth through certification reimbursement, dedicated professional development funding, and company-provided access to online learning platforms
Position Overview

The AI Engineer – Agentic Orchestration is a senior back-end engineer supporting a pilot program that is re-platforming a capabilities onto an enterprise AI platform to deliver predictive, decision-grade decision making capabilities to the customer. The primary responsibility for this role is architecting, building, and maintaining the production API integrations and data-pipeline components that flow data from disparate sources into the program’s Common Operating Picture (COP), and orchestrating the agentic AI workflows that operate on that data.

The engineer implements and versions robust interface contracts, builds high-throughput pipeline components that keep the COP reliable and current, writes rigorous integration and contract tests, tunes performance and reliability under heavy operational load, and authors clear technical documentation for long-term maintainability.

Key Responsibilities API Integration & Data Pipelines
  • Architect, build, and maintain production API integrations that ingest data from disparate operational, medical, and intelligence sources into the Common Operating Picture (COP).
  • Design, implement, and version robust interface contracts (REST/GraphQL, OpenAPI specifications) that external and internal producers can reliably build against.
  • Build and operate high-throughput, event-driven data-pipeline components (e.g., Apache Kafka) that keep the COP current, complete, and resilient to upstream failures.
  • Integrate data products, ontologies, and pipelines within the platform environment (e.g., Palantir Foundry) and cloud-hosted services in AWS Gov Cloud.
  • Implement enterprise authentication and authorization patterns (e.g., OAuth 2.0 / OIDC, SAML, PKI/CAC, attribute- and role-based access control) across integrated services.
Agentic Orchestration & AI Workflows
  • Design and implement agentic AI workflows that chain models, tools, and data services to move data from after-the-fact reporting toward predictive, decision-grade warning.
  • Build and maintain the orchestration layer (tool/function calling, prompt and workflow versioning, guardrails, retrieval, and evaluation harnesses) that exposes COP data to agents safely and repeatably.
  • Instrument agent behavior with logging, tracing, and evaluation metrics so outputs are explainable, testable, and auditable by program stakeholders.
  • Develop using open standards and…
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