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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** Cyber Sphere LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Cloud Computing Security, Continuous Integration, Data Deduplication, Drools, Python (Programming Language), Machine Learning, Medicaid Management Information Systems (MMIS), Azure Machine Learning, Search Technologies, Management of Software Versions, Backend, AI Platforms, Kubernetes, Low Latency, Azure AKS, Machine Learning Operations, Api Gateway, Restful APIs, Software Version Control, Api Management, Enterprise Service Bus - **Published:** August 7, 2026 - **Apply:** https://www.dice.com/job-detail/e4783d84-4d29-4603-aaa4-13257c8ee4bb ## About the Role 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. ## Description 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., 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., 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 ## Related Videos - [Azure AI Foundry for Developers: Open Tools, Scalable Agents, Real Impact](https://www.wearedevelopers.com/videos/1541-azure-ai-foundry-for-developers-open-tools-scalable-agents-real-impact) - [Azure-Well Architected Framework - designing mission critical workloads in practice](https://www.wearedevelopers.com/videos/1529-azure-well-architected-framework-designing-mission-critical-workloads-in-practice) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure) - [Azure-Well Architected Framework - Cost Optimization in practice](https://www.wearedevelopers.com/videos/2100-azure-well-architected-framework-cost-optimization-in-practice) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)