Principal Full Stack AI Software Developer

Company: JOIN OUR TEAM

Location: DC (Remote)

Salary: $159k - $201k per year

Type: Full-time

Remote: Yes

Posted: 2026-09-18

About this role

Job Type

Full-time

Description

NOTE: This opportunity is full-time employment position only (no 1099 or C2C engagements, or third parties or staffing agencies, please). The candidate MUST be a U.S. Citizen or Permanent Resident (Green Card holder). This is a remote opportunity; candidate must be based in the U.S.; have resided in the U.S. for at least 3 years in the past 5 years; ET time zone work schedule.

Daily Responsibilities

  • Serve as a senior, hands-on full-stack AI engineer, leading the design, development, and delivery of large-scale mission-critical AI systems.
  • Serve as a senior technical lead, defining AI and application architecture for the platform in partnership with and under the direction of the Director.
  • Establish enterprise modernization roadmaps aligned to mission outcomes, compliance, and scalability.
  • Lead architecture for distributed, cloud-native, and hybrid AI systems.
  • Define and enforce reference architectures, standards, and reusable frameworks.
  • Drive cross-program technical decision-making to ensure interoperability, security, and long-term sustainability.
  • Lead design, development, and deployment of advanced AI solutions, including large language models (LLMs) and foundation models, Retrieval-Augmented Generation (RAG) systems, agentic workflows, and orchestration frameworks.
  • Architect and implement scalable AI applications and services using Next.js, cloud-native APIs, and managed AI services across AWS, Azure, and GCP.
  • Build full-stack AI applications end to end, from user-facing interfaces to back-end services, APIs, and data layers.
  • Integrate AI and LLM capabilities into existing enterprise applications and legacy platforms (e.g., content management, case management, and records systems) via APIs, middleware, and event-driven patterns.
  • Oversee the full AI solution lifecycle: data pipelines, evaluation, deployment, and monitoring.
  • Drive LLM performance and cost optimization (e.g., caching, prompt an...

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