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Westinghouse Electric Company

Senior AI Solutions Architect – Azure

Westinghouse Electric Company

Senior AI Solutions Architect designing and implementing enterprise AI solutions using Microsoft Azure. Collaborating with stakeholders and building scalable AI/ML pipelines.

Posted 4/18/2026contractWadsworth • Texas • 🇺🇸 United StatesSenior💰 $60 - $75 per hourWebsite

Tech Stack

Tools & technologies
AzureERPPythonSQL

About the role

Key responsibilities & impact
  • Design and implement enterprise AI solutions on Microsoft Azure, leveraging services such as Azure OpenAI Service, Azure Machine Learning, and Azure AI Search to support intelligent applications and automation.
  • Build and maintain scalable AI/ML pipelines and MLOps workflows using Azure-native tools (or equivalent), enabling reliable model training, deployment, monitoring, and lifecycle management.
  • Develop retrieval-augmented generation (RAG) solutions, integrating enterprise data sources with large language models to enable contextual knowledge assistants and intelligent automation.
  • Collaborate with business and technology stakeholders to translate business requirements into AI solution architectures, delivering measurable improvements in operational efficiency, decision intelligence, and customer experience.
  • Architect and develop AI-enabled applications, chatbots, and intelligent agents using Microsoft Copilot Studio, Azure Bot Service, and the Microsoft Power Platform ecosystem.
  • Implement secure and compliant AI deployments within Azure environments, incorporating private networking, identity-based access controls, and responsible AI governance practices.
  • Integrate AI capabilities with enterprise platforms such as ERP, analytics, and workflow systems, ensuring seamless data flow and scalable service integration.
  • Establish and enforce best practices for AI development, including prompt engineering, model evaluation, versioning, performance monitoring, and cost optimization.
  • Evaluate emerging AI frameworks, libraries, and tools to continuously improve solution performance and accelerate AI innovation within the enterprise.
  • Provide technical leadership and mentorship to development teams on modern AI development patterns, including LLM orchestration, vector search architectures, and enterprise AI integration.

Requirements

What you’ll need
  • 8+ years experience in Microsoft Azure AI ecosystem, including Azure OpenAI Service, Azure Machine Learning, Azure AI Search, and Azure data services.
  • Experience with LLM architectures, prompt engineering, retrieval-augmented generation (RAG), and vector-based semantic search.
  • 3+ years of experience with Large Language Models (LLM) such as OpenAI, Azure OpenAI, Hugging Face, Anthropic, etc.
  • 5+ years of experience building production-grade AI assistants, chatbots, and intelligent agents using generative AI frameworks and orchestration tools.
  • Hands-on experience with Microsoft Copilot ecosystem, including Microsoft Copilot Studio and the Power Platform.
  • Proficiency in Python, SQL, and modern data engineering frameworks used in AI development pipelines.
  • MLOps principles, including model lifecycle management, CI/CD for ML, monitoring, and drift detection.
  • Experience integrating AI solutions with enterprise applications such as ERP, analytics platforms, and business process systems.
  • Strong understanding of security, compliance, and responsible AI governance, including data privacy and model transparency practices.
  • You will be required to work across engineering, data, and business teams to deliver scalable AI solutions.

Benefits

Comp & perks
  • Comprehensive Health and Income Protection Benefits
  • 401(k) Savings Plan
  • Paid Vacations for Qualifying Positions

ATS Keywords

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Hard Skills & Tools
Microsoft AzureAzure OpenAI ServiceAzure Machine LearningAzure AI SearchPythonSQLMLOpsprompt engineeringretrieval-augmented generationLarge Language Models
Soft Skills
technical leadershipmentorshipcollaborationcommunicationproblem-solvingorganizational skillsstakeholder engagementdecision intelligenceoperational efficiencycustomer experience