AI Architect

Elyon International•Washington, DC

1d•$65 - $70

About The Position

We are seeking an experienced AI Architect to provide technical leadership and architectural direction for artificial intelligence and machine learning solutions supporting a large-scale federal healthcare modernization program. The AI Architect will design secure, scalable, enterprise-ready AI solutions, including generative AI, machine learning, intelligent automation, and data-driven applications. The role works closely with technical leadership, data teams, cloud engineers, application teams, cybersecurity professionals, and government stakeholders to translate mission and business requirements into effective AI architectures.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Information Systems, or a related technical discipline.
7+ years of progressive technology experience, including significant experience in enterprise solution or cloud architecture.
Demonstrated experience architecting AI/ML solutions in complex enterprise environments.
Experience designing and implementing machine learning and generative AI solutions.
Strong knowledge of LLMs, NLP, machine learning, data engineering, and modern AI architecture.
Experience with public cloud AI/ML platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
Understanding of APIs, microservices, enterprise integration patterns, and distributed architectures.
Experience with Python and modern AI/ML frameworks.
Experience with MLOps, CI/CD, model deployment, monitoring, and lifecycle management.
Strong understanding of cloud security, identity and access management, and data protection principles.
Excellent communication, problem-solving, and stakeholder engagement skills.
Ability to meet applicable federal background investigation requirements.

Nice To Haves

Experience supporting federal healthcare, health IT, or other large federal programs.
Experience designing AI solutions within highly regulated or security-sensitive environments.
Experience with Azure AI Foundry/Azure AI services, Azure Machine Learning, AWS SageMaker, Databricks, or equivalent enterprise AI platforms.
Experience with generative AI and LLM architectures.
Experience implementing Retrieval-Augmented Generation (RAG) architectures and vector-based retrieval.
Experience with responsible AI and AI governance frameworks.
Experience working with healthcare data and interoperability environments.
Knowledge of FHIR, HL7, or other healthcare data standards.
Experience with enterprise data platforms, data lakes, APIs, and knowledge management solutions.
Relevant cloud or AI architecture certifications.

Responsibilities

Define enterprise AI architecture, technical strategy, standards, and solution patterns.
Design scalable AI, machine learning, and generative AI solutions for complex healthcare and enterprise use cases.
Lead architecture and technical design activities from proof of concept through production deployment.
Evaluate AI/ML models, platforms, frameworks, and cloud services based on business and technical requirements.
Design solutions leveraging large language models (LLMs), generative AI, natural language processing, machine learning, and intelligent automation.
Develop architectural approaches for Retrieval-Augmented Generation (RAG), enterprise search, knowledge management, and AI-enabled applications.
Design secure integration patterns between AI capabilities, enterprise applications, APIs, data platforms, and cloud environments.
Partner with data engineering and analytics teams to establish appropriate data architecture for AI solutions.
Provide architectural guidance for MLOps, model deployment, monitoring, versioning, and lifecycle management.
Establish reusable AI architecture patterns, frameworks, and technical standards.
Ensure AI solutions meet applicable federal security, privacy, accessibility, data governance, and compliance requirements.
Participate in architecture reviews, technical evaluations, and solution design sessions.
Collaborate with cybersecurity teams to identify and address risks associated with AI implementations.
Provide technical leadership and mentoring to AI/ML engineers, data scientists, developers, and other technical team members.
Communicate complex AI concepts and architectural recommendations to technical and non-technical stakeholders.
Support responsible AI practices, including appropriate governance, transparency, security, and human oversight.