AI Architect

Ampcus Inc; Chantilly (VA); On-site; USD 150,000 - 210,000; Full time

Job summary

Ampcus Inc. seeks an experienced AI architecture leader to define and drive the enterprise AI strategy.

You will collaborate with senior leadership to identify high-impact AI use cases, oversee secure platform development, and ensure governance across regulatory boundaries. You will architect scalable AI platforms, integrate autonomous Agentic AI workflows, and guide GenAI model deployment for policy drafting and stakeholder communication.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.
• 8+ years of experience in AI architecture, including hands-on development of ML/AI systems.
• Proven experience with GenAI platforms (e.g., Claude, Bedrock) and Agentic AI frameworks.
• Strong understanding of traditional AI/ML techniques, AI/MLOps, and data engineering.
• Familiarity with federal IT security standards and data governance practices.
• Excellent communication and stakeholder engagement skills.

Responsibilities

• Define and drive the AI architecture strategy aligned with the client’s mission and regulatory responsibilities.
• Collaborate with senior leadership to identify high-impact AI use cases across departments.
• Lead the development of an enterprise AI roadmap encompassing platform capabilities, governance, and scalability.
• Architect and oversee the implementation of a secure, scalable AI platform supporting diverse workloads.
• Integrate Agentic AI systems capable of autonomous task execution and decision-making within defined policy boundaries.
• Design and deploy GenAI models for document summarization, policy drafting, and stakeholder communication.
• Ensure interoperability with existing data infrastructure, cloud environments, and analytical tools.
• Build intelligent automation pipelines using traditional AI to streamline internal processes.
• Develop reusable AI components and APIs to support cross-functional teams.
• Establish monitoring and feedback mechanisms to ensure model performance, fairness, and compliance.
• Implement robust AI governance frameworks addressing ethical use, bias mitigation, and regulatory compliance.

Skills

AI architecture

GenAI platforms

AI/MLOps

data engineering

stakeholder engagement

communication

Education

Bachelor’s or Master’s degree in Computer Science/Data Science/AI/ML

Job description

• 1. Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.
• 2. 8+ years of experience in AI architecture, including hands-on development of ML/AI systems.
• Proven experience with GenAI platforms ((e.g., Claude, Bedrock) and Agentic AI frameworks.
• 1. Strong understanding of traditional AI/ML techniques, AI/MLOps, and data engineering.
• 2. Familiarity with federal IT security standards and data governance practices.
• 3. Excellent communication and stakeholder engagement skills.

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Preferred Skills

• 1. Experience in public sector or regulated environments.
• 2. Knowledge of economic modeling or financial systems.
• 3. Familiarity with LLM fine-tuning, retrieval-augmented generation (RAG), and multi-agent orchestration.
• 4. Experience with cloud-native AI platforms.

Key Responsibilities

Strategic Leadership

• 1. Define and drive the AI architecture strategy aligned with the client’s mission and regulatory responsibilities.
• 2. Collaborate with senior leadership to identify high-impact AI use cases across departments.
• 3. Lead the development of an enterprise AI roadmap encompassing platform capabilities, governance, and scalability.

Platform Development

• 1. Architect and oversee the implementation of a secure, scalable AI platform supporting diverse workloads (e.g., large language models, NLP, predictive analytics).
• 2. Integrate Agentic AI systems capable of autonomous task execution and decision-making within defined policy boundaries.
• 3. Design and deploy GenAI models for document summarization, policy drafting, and stakeholder communication.
• 4. Ensure seamless interoperability with existing data infrastructure, cloud environments, and analytical tools.

Automation & AI Enablement

• 1. Build intelligent automation pipelines using traditional AI (e.g., rule-based systems, ML models) to streamline internal processes.
• 2. Develop reusable AI components and APIs to support cross-functional teams.
• 3. Establish monitoring and feedback mechanisms to ensure model performance, fairness, and compliance.

Governance & Compliance

• 1. Implement robust AI governance frameworks addressing ethical use, bias mitigation, and regulatory compliance.
• 2. Collaborate with legal and policy teams to ensure AI systems adhere to federal standards and privacy regulations.
• 3. Contribute to the development of internal policies for responsible AI use.

• 1. Partner with data scientists, engineers, economists, and policy analysts to co-create AI solutions.
• 2. Mentor junior staff and promote AI literacy across the organization.
• 3. Represent the client in inter-agency AI initiatives and public forums.