We are hiring an AI/ML Engineer to design, build, and operate assistive AI/ML capabilities for a regulated provider enrollment and management platform.
The product supports provider enrollment, screening assist, document processing, guided intake, conversational support (chat/FAQ), and triage signals for Medicaid-style programs. AI/ML on this program is assistive only. Authoritative enrollment and screening decisions remain with deterministic business rules + human review. You will focus on production-grade AI features with strong governance, explainability, auditability, and HIPAA-aligned controls.
What you will do (core ownership)
- Design and implement conversational AI / RAG (provider FAQ, guided enrollment chat, policy-grounded answers) using Azure AI Foundry / Azure OpenAI, Copilot Studio, and vector search (Azure AI Search / embeddings).
- Build document intelligence pipelines (OCR, classification, form pre-fill / Smart-Start patterns) with Azure AI Document Intelligence, integrated into portal and backend services.
- Implement AI governance: prompt/version control, grounding and citations, hallucination controls, PII/PHI handling, human-in-the-loop checkpoints, and decision provenance suitable for audits and appeals.
- Establish MLOps: model/prompt registry, evaluation harnesses, CI/CD for AI assets, monitoring (quality, latency, cost), and environment promotion (Dev Test UAT Prod).
- Integrate AI services with the application stack (e.g., Power Platform, APIs / APIM, containerized services on AKS) using secure, least-privilege patterns.
- Define measurable acceptance criteria for AI features (accuracy, grounding rate, latency, cost, exception-queue rates) and iterate with product/BA partners.
- Optionally contribute assistive triage / scoring signals on Azure ML where models feed staff review not as the authority of record.
Required qualifications
- 4+ years in AI/ML engineering or applied ML in production systems.
- Hands-on with Azure AI: Azure OpenAI / AI Foundry, Azure ML, Azure AI Search, and/or Azure AI Document Intelligence.
- Strong experience building RAG systems (chunking, embeddings, retrieval evaluation, grounding, citation, safe refusal patterns).
- Proficiency in Python for AI/ML services; comfort consuming/producing REST APIs.
- Practical MLOps experience: versioning, automated evaluation, monitoring, and secure cloud deployment.
- Clear understanding of assistive vs authoritative AI in regulated workflows; ability to design human-in-the-loop systems.
- Working knowledge of HIPAA (or equivalent regulated-data) practices: least privilege, secrets management, PHI/PII handling in AI pipelines.
- Ability to turn product requirements into testable AI acceptance criteria and ship iteratively.
Nice to have
- Experience with Microsoft Power Platform AI patterns (Copilot Studio, adapters/connectors, Dataverse integration).
- Exposure to rules engines / DMN (e.g., Drools or similar) and how ML scores feed decision tables without becoming the decision authority.
- Entity resolution, fuzzy/phonetic matching, or deduplication assist patterns.
- Prior work in Medicaid / MMIS / MES, provider enrollment, credentialing, or other CMS-regulated healthcare systems.
- Experience supporting responsible AI / GenAI disclosure documentation for public-sector or regulated programs (supporting Architecture/Proposal not owning RFP authorship).
- Familiarity with Kubernetes/AKS, API gateways, and Azure network isolation for AI workloads.
- Domain exposure (as examples only not required ownership) such as:
- Screening vendor strategy evaluating aggregator / CVO / sanctions feeds and integration patterns via an enterprise service bus
- DMN / business rules ACA categorical risk tiers, appeal-defensible decision tables, rule versioning
- Network adequacy spatial coverage analytics, geo tooling, or adequacy reporting feeds
- Multi-state productization configuring state-specific adapters while keeping a shared AI platform core
Success in the first 6 12 months
- Production-ready assistive chat/RAG + FAQ with grounding, audit logging, and safe fallbacks.
- Reliable document OCR / extraction path with measurable accuracy and clear exception handling.
- Documented AI governance model (authority boundary with rules + humans, provenance, evaluation gates) accepted by Architecture and Security.
- Operable MLOps baseline on Azure (registry, promotion path, monitoring, cost controls).
Tech environment (illustrative)
Area | Typical stack |
Cloud | Azure Commercial; HIPAA BAA; FedRAMP-authorized services where applicable |
AI / ML | Azure AI Foundry / OpenAI, Copilot Studio, Azure ML, Document Intelligence, AI Search |
App / integration | Power Platform, APIM, AKS, secure adapters |
Authoritative decisioning | Rules engine (DMN) + human review (owned by rules/platform teams) |
Collaboration | GitHub / Azure DevOps |
Soft skills
- Communicates clearly with architects, BAs, security, and engineering partners.
- Pragmatic: ships governed, measurable AI not science projects.
- Comfortable collaborating across integration and rules teams without needing to own their domains.
- Documents decisions so they are audit-ready.
Education
Bachelor s or Master s in Computer Science, Data Science, Machine Learning, or equivalent practical experience.
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