Our client has developed a new solution that enables eye doctors to remotely examine patients via video conference, controlling the phoropter at the site.  This fantastic technology consolidates images/data to provide a more comprehensive view. 

Our Client is a fast-growing health-tech company modernizing eye care nationwide. We’re seeking a Senior Web & Azure ML Engineer who can own full-stack delivery and the ML lifecycle in Azure—from data to deployment—to power our Digital Tele-Optometry platform and AI features.

 What You’ll Do

• End-to-end ML in Azure: Train, evaluate, and deploy models using Azure Machine Learning Studio & the Azure ML SDK (Python). Package models as real-time/batch endpoints and integrate them into ASP.NET/Core services
• MLOps at scale: Build CI/CD for ML with Azure DevOps (pipelines, artifacts, environments).
• Responsible AI & monitoring: Performance telemetry via AML monitoring/Azure Monitor.
• Production integration: Expose model inference securely to .NET/API backends (App Service/AKS); optimize latency, throughput, and cost.
• Web application delivery: Design, build, and scale cloud-native web apps (C#/ASP.NET, SQL, JavaScript) in Microsoft Azure—including real-time experiences (SignalR).
• Operational excellence: Establish incident playbooks, logging/alerting (App Insights
• Work closely with web devs, designers, and cross-functional teams to deliver new functionality for a growing, AI-enabled platform.

Tools & Stack You’ll Use

• Azure AML (workspaces, compute, registry, endpoints), App Service/AKS, /App Insights
• Azure DevOps (Repos/Pipelines/Boards), Git, CI/CD for app & model, Infrastructure as Code
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• JavaScript/TypeScript (frameworks as applicable).

MUST HAVE Qualifications

·       7+ years building web applications (C#/ASP.NET, Web API, JavaScript/TypeScript).

·       5+ years on Microsoft Azure with production workloads (App Service, SQL, Storage, networking).

·       Azure ML hands-on experience: training and deploying models with Azure ML Studio/SDK, model registry, endpoints, and monitoring.

·       MLOps with Azure DevOps: pipelines for data + model CI/CD, gated releases, infrastructure-as-code (Bicep/Terraform or ARM), and secrets management.

·       Data/Model fundamentals: feature engineering, evaluation, cross-validation, experiment tracking.

·       SQL proficiency and production API integration of ML services into .NET apps running in Azure.

·       Strong debugging/issue resolution skills across web apps and services.

·       Experience ( or some exposure)  with SignalR, pub-sub architectures, embedded video conferencing, and real-time UX.

·       Experience building Whisper or speech-enabled applications/pipelines. (NICE TO HAVE)

·       Telerik / Kendo / DevExpress component libraries.

·       Exposure to Azure Kubernetes Service (AKS), Docker.