Azure ML Architect / Consultant (Azure OpenAI, LLMs, Azure App Service, Containers, Docker, AKS & Web Applications on Azure)
Fully Remote Job
3 Months Contract
We are looking for an Azure ML Architect / Consultant on a contract, remote basis for approximately 1-3 months. The consultant will assess current predictive modeling workflows and design an Azure-based ML architecture to streamline model scoring, deployment, and integration with Salesforce. They will deliver best practices, documentation, training, and cost/monitoring guidance to enable the internal team to operate and scale models and pipelines. The engagement may include a proof-of-concept migration of one model and recommendations for hosting self-service web apps and optional LLM usage guidance.
Responsibilities:
Assess existing predictive models, local workflows, and data sources and document architectural requirements.
Recommend Azure ML architecture and specific Azure services required based on best practices.
Provide guidance and documentation for deployment, monitoring, and cost forecasting of Azure ML workloads.
Advise on data pipeline design from Oracle to Azure and patterns to integrate scored outputs into Salesforce.
Deliver training, demos, and knowledge transfer to internal team members and produce runbooks.
Develop a plan and assist with configuration/setup of the Azure environment and subscription considerations.
Support a proof-of-concept migration of one model to Azure as a template for future models.
Advise on hosting options for custom self-service web apps and containerization strategies.
Required Skills & Experience:
Proven experience designing and deploying Azure ML solutions and ML operationalization.
Deep knowledge of Azure services (Azure ML, Data Factory or Pipelines, compute and storage) and cost drivers.
Experience building or advising on data pipelines from Oracle or similar data warehouses into cloud environments.
Familiarity with integrating ML outputs into Salesforce (including MuleSoft or other integration patterns).
Strong ability to produce documentation, runbooks, training materials, and conduct knowledge transfer.
Expertise in monitoring, governance, security, and permissions for Azure ML workloads.
Plusses:
Experience with Azure OpenAI / LLM integrations and governance for AI usage.
Hands-on experience hosting web applications on Azure (AKS, containers, App Service) for self-service tools.
Prior experience in higher education or nonprofit analytics environments.
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