AI/ML Engineer
Remote
Long Term
W2
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.
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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.
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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.
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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:
o Screening vendor strategy evaluating aggregator / CVO / sanctions feeds and integration patterns via an enterprise service bus
o DMN / business rules ACA categorical risk tiers, appeal-defensible decision tables, rule versioning
o Network adequacy spatial coverage analytics, geo tooling, or adequacy reporting feeds
o Multi-state productization configuring state-specific adapters while keeping a shared AI platform core
Munesh
,
CYBER SPHERE LLC
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