AI Engineer at Axiologic Solutions LLC | DC, US | Rezi

AI Engineer at Axiologic Solutions LLC

AI Engineer

Axiologic Solutions LLC · DC, US

2 weeks ago

AI Engineer

Axiologic Solutions LLC · DC, US

16 days ago
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About the Role

Axiologic Solutions is seeking an experienced AI Engineer to support AI/ML architecture design and engineering, including enterprise architecture baseline development, solution architecture, and architecture governance. The AI Engineer will support AI model governance, cloud-based data pipeline architectures, and agentic AI system development. This role will also support the development of agent evaluation frameworks, prompt engineering governance standards, AI model fine-tuning pipeline architectures, and AI governance artifacts such as model risk scorecards, agentic system boundary definitions, and automated compliance reporting for AI deployments.

Responsibilities

  • Support AI/ML architecture design and engineering across enterprise environments.
  • Assist with enterprise architecture baseline development, solution architecture, and architecture governance.
  • Support AI model governance frameworks and data pipeline architectures leveraging cloud infrastructure.
  • Assist with agentic AI development, including agent evaluation frameworks and intelligent workflows.
  • Support prompt engineering governance standards and AI model fine-tuning pipeline architectures.
  • Develop AI governance artifacts, including model risk scorecards, agentic system boundary definitions, and automated compliance reporting.
  • Collaborate with technical teams and stakeholders to ensure AI solutions align with enterprise architecture, security, and mission requirements.

Requirements

  • 5+ years of relevant professional experience in AI/ML engineering, artificial intelligence, data science, software engineering, or a related technical field.
  • Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
  • Experience with AI/ML architecture, model development, deployment, and governance.
  • Experience with cloud infrastructure and AI/ML data pipeline architectures.
  • Familiarity with agentic AI, prompt engineering, model evaluation, and model fine-tuning.
  • Ability to develop technical documentation and AI governance artifacts.
  • AWS or Microsoft Azure AI/ML specialty certification preferred.
  • Experience with agentic AI systems, AI model risk management, or responsible AI governance.
  • Experience supporting enterprise-scale AI/ML solutions in complex technical environments.
  • TS/SCI eligibility required for the position. Candidates with an active TS/SCI are preferred; candidates with a recent active TS may also be considered for SCI upgrade based on eligibility and program requirements.

Skills

  • AI/ML engineering
  • Artificial intelligence
  • Data science
  • Software engineering
  • AI/ML architecture
  • Model development
  • Model deployment
  • Model governance
  • Cloud infrastructure
  • AI/ML data pipeline architectures
  • Agentic AI
  • Prompt engineering
  • Model evaluation
  • Model fine-tuning
  • Technical documentation
  • AI governance artifacts
  • AWS AI/ML
  • Microsoft Azure AI/ML
  • AI model risk management
  • Responsible AI governance

Experience Level

  • 5+ years of relevant professional experience

Education Level

  • Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

Salary/Compensations

  • $90,000 – $186,000

About the Company

  • Axiologic Solutions is seeking an experienced AI Engineer to support AI/ML architecture design and engineering, including enterprise architecture baseline development, solution architecture, and architecture governance. The AI Engineer will support AI model governance, cloud-based data pipeline architectures, and agentic AI system development. This role will also support the development of agent evaluation frameworks, prompt engineering governance standards, AI model fine-tuning pipeline architectures, and AI governance artifacts such as model risk scorecards, agentic system boundary definitions, and automated compliance reporting for AI deployments.