Design and build secure, cloud-native AI applications using LLMs, RAG, and Amazon Bedrock to provide AI-assisted search, summarization, and clinical question-answering; integrate with enterprise healthcare systems using FHIR/HL7, implement vector search and prompt orchestration, ensure Fed security/privacy compliance and CI/CD, and support testing, monitoring, and governance for production readiness.
VetsEZ is seeking a Senior AI Application Engineer to design, develop, and implement enterprise Artificial Intelligence (AI) solutions supporting the Department of Veterans Affairs (VA). The initial assignment will support the Joint Longitudinal Viewer (JLV) AI Summarization initiative, delivering a secure, governed AI-assisted search and summarization capability within an approved test environment utilizing Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Amazon Bedrock, while supporting clinical evaluation and future production readiness.
Working closely with AI Solution Architects, Product Owners, cybersecurity teams, DevSecOps engineers, and clinical stakeholders, this individual will develop secure, scalable, and maintainable AI-powered applications that integrate seamlessly with existing enterprise healthcare systems while ensuring compliance with Federal security, privacy, and AI governance requirements.
Responsibilities:
- Design, develop, and implement enterprise AI applications utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
- Develop AI-assisted search, summarization, and question-answering capabilities using Amazon Bedrock.
- Build reusable AI services supporting prompt orchestration, document retrieval, and response generation.
- Implement prompt engineering and source-grounding strategies to improve AI accuracy, consistency, traceability, and clinical relevance, including source links that support human verification.
- Optimize AI performance while balancing response quality, latency, and operational cost.
- Design and develop secure, scalable cloud-native applications utilizing modern software engineering practices.
- Develop RESTful APIs and backend services supporting AI capabilities and enterprise integrations.
- Implement approved document retrieval, vector search, and semantic search capabilities within the selected patient context and CHSD document set.
- Develop automated unit, integration, and functional tests and repeatable AI evaluations for groundedness, retrieval quality, and clinical relevance supporting AI-enabled applications.
- Troubleshoot software defects, optimize application performance, and support activities within the approved test environment and for future production readiness.
- Integrate AI capabilities into existing enterprise healthcare applications and clinical workflows.
- Develop secure interfaces utilizing REST APIs and modern integration patterns.
- Support interoperability utilizing healthcare standards including FHIR, HL7, and CCD.
- Collaborate with Solution Architects and engineering teams to implement scalable and maintainable application designs.
- Participate in code reviews and promote software engineering best practices across the development team.
- Develop secure software in accordance with Federal cybersecurity and privacy requirements, including approved data-retention and purge controls.
- Support CI/CD pipelines, automated deployments, and cloud-native operational practices.
- Implement logging, monitoring, audit capabilities, and operational telemetry, including model and prompt version tracking, usage and cost monitoring, and controls to detect model, prompt, retrieval, and data drift.
- Support application security scanning, vulnerability remediation, and activities within the approved test environment and for future production readiness.
- Incorporate Responsible AI, Human-in-the-Loop (HITL), and AI governance principles into application development.
- Collaborate with architects, product owners, clinicians, cybersecurity teams, and Government stakeholders throughout the software development lifecycle.
- Participate in Agile ceremonies including Sprint Planning, backlog refinement, Sprint Reviews, and Retrospectives.
- Contribute to technical documentation, implementation guides, and software design artifacts.
- Present technical solutions and implementation approaches to project leadership and stakeholders.
Requirements:
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, Artificial Intelligence, Data Science, or a related technical field, or equivalent experience.
- 8+ years developing enterprise software applications.
- 5+ years developing cloud-native applications utilizing AWS or comparable cloud platforms.
- Demonstrated experience developing Artificial Intelligence, Machine Learning, or Generative AI solutions.
- Experience implementing applications utilizing Amazon Bedrock or similar enterprise AI platforms.
- Experience developing enterprise REST APIs and cloud-native application services.
- Amazon Bedrock and AWS cloud services
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Prompt engineering and AI evaluation
- Python, Java, or C#
- REST APIs and JSON
- Vector databases, embeddings, and semantic search
- Git, CI/CD, and DevSecOps
- Healthcare interoperability (FHIR, HL7, CCD)
Additional Qualifications:
- Strong understanding of modern software engineering principles and cloud-native application development.
- Experience developing scalable, secure, and maintainable enterprise applications.
- Excellent analytical, troubleshooting, and problem-solving skills.
- Strong written and verbal communication skills with the ability to collaborate across multidisciplinary engineering teams.
- Ability to obtain and maintain a Government Public Trust clearance.
- Experience supporting the Department of Veterans Affairs (VA), Department of Defense (DoD), or other Federal healthcare organizations.
- Experience developing AI-enabled clinical workflow, information-retrieval, or clinician-support applications requiring human validation.
- Experience implementing vector search, embeddings, semantic search, and prompt orchestration.
- Knowledge of Responsible AI, NIST AI Risk Management Framework (AI RMF), NIST SP 800-53, FISMA, and FedRAMP.
- Familiarity with clinical terminology standards including SNOMED CT, ICD-10, RxNorm, and LOINC.
- AWS Developer, AWS AI, Machine Learning, or other AWS cloud certifications are highly desirable.
Benefits:
- Medical, Dental, and Vision Insurance
- 401(k) with Employer Match
- Paid Time Off plus Federal Holidays
- Corporate Laptop
- Professional Development and Training Opportunities
- Remote Opportunity
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status.
Sorry, we are unable to offer sponsorship at this time.
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