AI/ML Engineer

Job Description AI/ML Engineer

Domain: Healthcare / Medicaid Provider Management (MES / MMIS)

Type: Full-Time with Infinite Computer Solutions Cloud: Microsoft Azure

About the role

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.