Senior Azure AI Foundry Engineer

This is a Full Time Job

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

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

Values in Action