Sr. Web & Azure ML Engineer

OH200-2752728 · · Janis Mitchell

Job Description

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. JavaScript/TypeScript (frameworks as applicable). Required 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 with SignalR, pub-sub architectures, embedded video conferencing, and real-time UX. Experience building Whisper or speech-enabled applications/pipelines. Telerik / Kendo / DevExpress component libraries. Exposure to Azure Kubernetes Service (AKS), Docker.