Senior Copilot & Azure AI Foundry Engineer

Hearst•Charlotte, NC

13d

About The Position

Hearst Television (HTV) is hiring a Senior Copilot & Azure AI Foundry Engineer to lead the build of our next-generation platform. You will be instrumental in designing, building, stabilizing, and scaling enterprise-grade AI solutions using Microsoft’s Azure AI ecosystem. As a senior technical resource with expertise in AI architectures, MLOps, and orchestration frameworks, you will evaluate and enhance our Copilot Studio agents, ensuring they are scalable, accurate, maintainable, and aligned with modern Azure AI best practices. This role is central to our mission of transforming media sales through prescriptive, actionable intelligence. This position does not provide sponsorship.

Requirements

8+ years in software architecture or cloud engineering, with at least 3+ years specifically focused on production-grade AI/ML solutions.
Strong hands-on experience with Microsoft Copilot Studio and Azure AI Foundry (formerly Azure AI Studio).
Expert-level Python for AI/ML development and automation; additional experience in C#, .NET, or Java is preferred.
Solid understanding of Generative AI, prompt engineering, RAG architectures, embeddings, and vector databases.
Demonstrated experience with model lifecycle management, CI/CD pipelines, and designing resilient, serverless AI workloads.
Proven ability to troubleshoot, refactor, and optimize existing AI solutions to meet 99.8% accuracy targets.
Ability to lead architectural discussions with executive stakeholders and translate business needs into technical AI requirements.

Responsibilities

Analyze and refactor Copilot Studio agents to eliminate redundancies, improve routing efficiency, and streamline topic structures for better maintainability.
Transition static, rule-based topics into grounded generative experiences and replace legacy ingestion methods with modern Azure AI approaches.
Enhance solution accuracy by integrating Azure AI Foundry capabilities, including Cognitive Search with vector embeddings and custom RAG endpoints.
Implement high-level capabilities like multi-step reasoning, context persistence, summarization, and function calling for complex user interactions.
Design and manage scalable AI/ML pipelines, covering everything from model fine-tuning and versioning to CI/CD, monitoring, and security.
Serve as the technical lead to design scalable architectures, guiding best practices and ensuring internal teams can maintain the solution post-handoff.
Develop infrastructure-as-code for resilient workloads and produce comprehensive documentation, including troubleshooting guides and operational playbooks.