Generative AI Engineer

Accountabilities: Design, develop, test, deploy, and maintain generative AI solutions that ground large language model responses in authoritative healthcare program documentation. Build and maintain document ingestion pipelines covering parsing, chunking, metadata tagging, embedding generation, and vector or semantic search indexing. Translate user needs and stakeholder requirements into effective prompts, context strategies, system requirements, and evaluation criteria in collaboration with cross-functional teams and subject matter experts. Implement AI responses that include source citations and direct links to underlying documentation, ensuring that users can trace answers back to authoritative materials. Design and implement responsible AI guardrails, including content filtering, scope restrictions, PII and PHI protections, and appropriate refusal handling for out-of-scope questions. Develop evaluation frameworks and test datasets to measure retrieval quality, answer accuracy, groundedness, consistency, and overall system performance. Continuously monitor, evaluate, and tune AI solutions to improve reliability while balancing accuracy, latency, cost, security, and specific use-case requirements. Design AI applications capable of supporting multiple foundation models and providers and evaluate model performance against business and technical requirements. Deploy and operate solutions using approved AWS services and within applicable federal security controls, contributing to documentation required for security authorization and AI governance reviews. Implement logging, monitoring, and audit trails that support transparency, operational oversight, cost management, and responsible AI governance. Write clean, reusable, well-documented code and contribute to code reviews, automated testing, deployment processes, and ongoing system maintenance. Collaborate with technical and non-technical stakeholders to communicate AI capabilities, limitations, risks, and performance clearly. Stay current with emerging generative AI technologies, foundation models, development frameworks, security practices, and federal AI guidance. Requirements: You have a master’s degree in computer science, data science, engineering, or another relevant technical field. You have at least 5 years of experience developing software and cloud-based solutions, including recent hands-on experience designing and deploying LLM or generative AI applications. You have strong proficiency in Python and experience working with modern LLM APIs, SDKs, orchestration frameworks, and AI/ML libraries such as Hugging Face, LangChain, LlamaIndex, or equivalent technologies. You have hands-on experience designing and deploying generative AI systems involving embeddings, vector databases, and hybrid or semantic search. You have experience with AWS AI and data services such as Amazon Bedrock, Amazon OpenSearch, Amazon Kendra, SageMaker, Lambda, or S3. You have practical experience with prompt engineering, context engineering, LLM evaluation, and techniques for reducing hallucinations and ensuring responses remain grounded in source material. You understand how to evaluate and select foundation models based on factors including accuracy, latency, cost, security, and specific application requirements. You have experience working within Agile software development environments and understand modern software engineering and delivery practices. You have experience building and maintaining CI/CD pipelines and deploying infrastructure using tools such as GitHub Actions, CloudFormation, Terraform, and/or Jenkins. You have strong problem-solving, communication, and collaboration skills, including the ability to explain complex AI capabilities and limitations to non-technical stakeholders. You can work effectively both independently and as part of a multidisciplinary team, with strong attention to detail and a professional approach to documentation and presentation. Experience supporting federal health programs, including program policy, regulatory guidance, or customer and stakeholder support operations, is highly desirable. Experience deploying AI solutions in federal environments, including FedRAMP-authorized services, ATO or security authorization processes, and NIST 800-53 controls, is preferred. Familiarity with responsible AI and federal AI governance frameworks, including the NIST AI Risk Management Framework and OMB AI guidance, is advantageous. Knowledge of healthcare security and compliance requirements such as HIPAA and HITECH is preferred. Experience building conversational or search interfaces, including familiarity with Section 508 accessibility requirements, is a plus. Relevant AWS certification, such as AWS Certified Machine Learning Engineer – Associate, AWS Certified AI Practitioner, Generative AI Developer – Professional, Solutions Architect, or an equivalent credential, is desirable. Benefits: Opportunity to work on mission-critical generative AI solutions supporting healthcare organizations and access to authoritative program information. Hands-on exposure to advanced LLM technologies, retrieval-augmented generation, semantic search, AI evaluation, and responsible AI practices. Opportunity to work with AWS cloud services and modern AI engineering tools and frameworks. Collaborative environment involving engineers, analysts, project managers, subject matter experts, and client stakeholders. Opportunity to contribute to secure and responsible AI solutions operating within federal security and governance requirements. Professional growth through exposure to emerging AI technologies, federal AI guidance, and complex healthcare use cases.