Note: The job is a remote job and is open to candidates in USA. The company is hiring an AI/ML Engineer to build and operate assistive AI/ML capabilities for a regulated Medicaid-style provider enrollment and management platform. The role focuses on conversational AI, document intelligence, governance, MLOps, secure Azure integrations, and human-in-the-loop workflows while maintaining auditability, explainability, and HIPAA-aligned controls.
Responsibilities
- 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
Skills
- 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
- 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
Benefits
- Remote work arrangement
- Long Term Contract
Company Overview
Company H1B Sponsorship