{"id":2039734,"url":"https://alion.io/job/magpie-health-analytics-generative-ai-engineer","title":"Generative AI Engineer","company":{"id":449915,"name":"Magpie Health Analytics","domain":"magpiehealthanalytics.com","url":"https://alion.io/company/magpie-health-analytics","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Breezy","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Baltimore, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":131000,"max_usd":237000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":314},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Context Engineering","optional":false},{"name":"Embeddings","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Hugging Face","optional":false},{"name":"Jenkins","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenSearch","optional":false},{"name":"Python","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Terraform","optional":false},{"name":"FedRAMP","optional":true},{"name":"HIPAA","optional":true},{"name":"Machine Learning","optional":true},{"name":"NIST 800-53","optional":true},{"name":"NIST AI RMF","optional":true}],"status":"live","first_seen_at":"2026-10-07T17:10:03Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-19T06:29:51Z","board_verified":true,"closed_at":null,"days_open":11,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":11},"description":"Position Summary:\nMagpie Health Analytics is seeking an experienced AI Engineer to design, build, and operate secure, trustworthy generative AI solutions that help our healthcare clients deliver faster, more consistent access to authoritative program guidance.\nKey Responsibilities:\nOur clients are government health payers. Your primary responsibility is to design, develop, test, deploy, and maintain an AI-enabled decision-support capability that allows users to search and navigate large bodies of program documentation (statutes, regulations, rules, and guidance) using natural language. The solution will first support internal customers and will later be extended to external, public-facing users. You will work in collaboration with project managers, data engineers, full stack developers, analysts, and client stakeholders to ensure the successful implementation of the client solution. The AI Engineer duties include:\nDesign, develop, and maintain generative AI solutions that ground large language model (LLM) responses in authoritative source documentation.\nBuild and maintain document ingestion pipelines, including parsing, chunking, metadata tagging, embedding generation, and vector/semantic search indexing.\nCollaborate with cross-functional teams and subject matter experts to understand user needs and translate them into requirements, prompts, and evaluation criteria.\nImplement responses with source citations and direct links to the underlying documentation to ensure traceability.\nDesign and implement guardrails, including content filtering, scope restrictions, PII/PHI protections, and refusal handling for out-of-scope questions.\nDevelop evaluation frameworks and test sets to measure retrieval quality, answer accuracy, groundedness, and consistency; monitor and tune performance over time.\nDeploy and operate solutions within approved AWS services and federal security controls, supporting documentation needed for security authorization and AI governance reviews.\nImplement logging, monitoring, and audit trails to support transparency, cost management, and responsible AI oversight.\nWrite clean, reusable, and well-documented code, and participate in code reviews, testing, and deployment processes.\nStay up to date with emerging AI technologies, models, and federal AI guidance.\nEducational Requirements:\nA master’s degree in computer science, data science, engineering, or another relevant field. \nQualifications:\n5+ years of experience developing software and cloud-based solutions, with demonstrated recent experience designing and deploying LLM or generative AI applications.\nStrong proficiency in Python and experience with modern LLM APIs, SDKs, orchestration frameworks, and AI/ML libraries (e.g., Hugging Face, LangChain, LlamaIndex, or equivalent).\nHands-on experience designing and deploying generative AI solutions, including embeddings, vector databases, and hybrid/semantic search.\nExperience with AWS AI and data services such as Amazon Bedrock, Amazon OpenSearch, Amazon Kendra, SageMaker, Lambda, or S3.\nExperience with prompt and context engineering, LLM evaluation, and techniques for reducing hallucinations and ensuring answers are grounded in source material.\nDesigning AI applications to support multiple foundation models/providers and evaluating models based on accuracy, latency, cost, security, and use-case requirements.\nExperience with Agile software development methodologies.\nExperience with building and maintaining CI/CD pipelines, testing, and deploying infrastructure using services such as GitHub Actions, CloudFormation, Terraform, and/or Jenkins.\nExcellent problem-solving and communication skills, including the ability to explain AI capabilities and limitations to non-technical stakeholders.\nAbility to work independently and in a team, with strong attention to detail and presentation.\nPreferred Qualifications:\nExperience supporting federal health programs, including program policy, regulatory guidance, and customer or stakeholder support operations.\nExperience deploying AI solutions in federal environments, including FedRAMP-authorized services, ATO/security authorization processes, and NIST 800-53 controls.\nFamiliarity with responsible AI and federal AI governance frameworks (e.g., NIST AI Risk Management Framework, OMB AI guidance).\nUnderstanding of security and compliance regulations in healthcare, such as HIPAA and HITECH.\nExperience building conversational or search interfaces, including Section 508 accessibility compliance.\nRelevant AWS certification (e.g., AWS Certified Machine Learning Engineer - 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