Remote AI Solutions Architect – Enterprise LLM & RAG Leader

VetsEZ

Charleston (SC)

On-site

USD 150,000 - 190,000

Full time

14 days+

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Benefits offered by this job

Medical, Dental, and Vision Insurance

401(k) with Employer Match

Paid Time Off plus Federal Holidays

Corporate Laptop

Professional Development and Training

Remote Opportunity

Job summary

VetsEZ is seeking a Senior AI Solutions Architect to lead enterprise AI design and implementation for VA projects. You will own AI architecture across the JLV contract, delivering secure MVPs using RAG and LLMs in a compliant AWS environment.

Responsibilities include modeling data flows, interfaces, and orchestration, while guiding teams and ensuring regulatory alignment—FHIR/HL7/CCD. You will collaborate with DevSecOps, evaluate technologies, and shape scalable AI pipelines with governance and

Qualifications

Bachelor's degree in Computer Science, Software Engineering, Information Systems, AI, Data Science, or related field, or equivalent experience.
10+ years designing enterprise software solutions.
5+ years designing cloud-native architectures utilizing AWS or comparable cloud platforms.
Demonstrated experience architecting AI, ML, or Generative AI solutions.
Experience with Amazon Bedrock or similar AI platforms.
Experience leading architecture across multidisciplinary engineering teams.
Familiarity with healthcare interoperability standards (FHIR/HL7/CCD).

Responsibilities

Lead the architecture, design, and implementation of enterprise AI solutions using LLMs and RAG.
Design scalable, secure AI architectures for enterprise healthcare applications.
Define solution architecture, data flows, interfaces, AI orchestration, and integration patterns.
Evaluate AI technologies to support current and future project needs.
Ensure architecture supports future phases without expanding MVP scope.
Design AI solutions leveraging AWS cloud services and Bedrock.
Architect secure AI pipelines for PHI/PII, document processing, vector search, and summarization.
Define model selection, prompt management, retrieval optimization, and performance tuning.
Monitor for model and data drift; address degradation in accuracy or safety.
Collaborate with DevSecOps for deployment automation and operations.
Provide architectural guidance throughout the SDLC and present to stakeholders.