Generative AI Engineer

Job not on LinkedIn

🔥 5 minutes ago

🦀 Maryland – Remote

🦀 Maryland – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🗣️ LLM Engineer

👻 Ghost score 15%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Magpie Health Analytics

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

🏥 Healthcare

🏛️ Government

🤖 Artificial Intelligence

Healthcare • Government • Artificial Intelligence

Magpie Health Analytics is a mission-focused small business that provides data-driven oversight, program integrity, and modernization services for U. S. federal health agencies (notably CMS and HHS). The company offers subject-matter-expert support for risk adjustment, encounter data, payment integrity (fraud/waste/abuse) analytics, RADV/CPI audit support, and policy-aligned modeling, along with cloud-native health IT and data engineering (Snowflake, AWS Glue/S3/Lambda, IaC, CI/CD). Magpie also develops AI/ML and GenAI tools—RAG, NLP, anomaly detection, explainable dashboards, and AI agents—to support audits, regulatory research, and automated analytic workflows. It operates as a GSA MAS schedule holder and SBA-registered small business and is positioned as a trusted subcontractor and technical partner for federal healthcare oversight and modernization.

📋 Description

• Design, develop, test, deploy, and maintain an AI-enabled decision-support capability for searching and navigating statutes, regulations, rules, and guidance using natural language • Design, develop, and maintain generative AI solutions grounded in authoritative source documentation • Build and maintain document ingestion pipelines, including parsing, chunking, metadata tagging, embedding generation, and vector/semantic search indexing • Collaborate with project managers, data engineers, full stack developers, analysts, subject matter experts, and client stakeholders • Translate user needs into requirements, prompts, and evaluation criteria • Implement responses with source citations and direct links to underlying documentation • Design and implement guardrails, including content filtering, scope restrictions, PII/PHI protections, and refusal handling • Develop evaluation frameworks and test sets for retrieval quality, answer accuracy, groundedness, and consistency • Monitor and tune performance over time • Deploy and operate solutions within approved AWS services and federal security controls • Support documentation for security authorization and AI governance reviews • Implement logging, monitoring, and audit trails for transparency, cost management, and responsible AI oversight • Write clean, reusable, and well-documented code • Participate in code reviews, testing, and deployment processes • Stay current with emerging AI technologies, models, and federal AI guidance

🎯 Requirements

• Master’s degree in computer science, data science, engineering, or another relevant field • 5+ years of experience developing software and cloud-based solutions • Recent experience designing and deploying LLM or generative AI applications • Strong proficiency in Python • Experience with modern LLM APIs, SDKs, orchestration frameworks, and AI/ML libraries, such as Hugging Face, LangChain, or LlamaIndex • Hands-on experience with embeddings, vector databases, and hybrid/semantic search • Experience with AWS AI and data services, such as Amazon Bedrock, Amazon OpenSearch, Amazon Kendra, SageMaker, Lambda, or S3 • Experience with prompt and context engineering, LLM evaluation, hallucination reduction, and grounded answers • Experience designing AI applications supporting multiple foundation models/providers and evaluating accuracy, latency, cost, security, and use-case fit • Experience with Agile software development methodologies • Experience building and maintaining CI/CD pipelines, testing, and infrastructure deployment using GitHub Actions, CloudFormation, Terraform, and/or Jenkins • Excellent problem-solving and communication skills • Ability to explain AI capabilities and limitations to non-technical stakeholders • Ability to work independently and in a team, with strong attention to detail and presentation • Preferred: Experience supporting federal health programs • Preferred: Experience deploying AI solutions in federal environments, including FedRAMP-authorized services, ATO/security authorization processes, and NIST 800-53 controls • Preferred: Familiarity with responsible AI and federal AI governance frameworks • Preferred: Understanding of HIPAA and HITECH • Preferred: Experience building conversational or search interfaces, including Section 508 accessibility compliance • Preferred: Relevant AWS certification or equivalent

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