AI Engineer

ted Generation) patterns, fine-tuning, and multi-agent systems.

• Develop intelligent workflows using Azure Agentic AI and Semantic Kernel to automate complex business processes.

• Integrate AI capabilities with Microsoft 365, SharePoint, Teams, Power Platform, and third-party enterprise applications.

• Implement Azure AI Document Intelligence solutions for automated document processing and data extraction.

• Build and optimize vector databases and semantic search using Azure AI Search for knowledge management systems.

• Establish MLOps practices using Azure Machine Learning and Azure DevOps for automated model deployment and monitoring.

• Design and develop scalable APIs and microservices for AI model deployment and integration.

• Create data pipelines to support AI model training and real-time inference.

• Define AI solution standards, best practices, and governance frameworks including security and compliance.

• Mentor AI engineers and developers, providing technical guidance and code reviews.
  Lead proof-of-concept projects to evaluate new AI technologies and their business value.

• Collaborate with architects, data scientists, and stakeholders to define AI roadmaps and technical strategies.

• Stay current with AI technologies and Azure AI service updates to drive continuous improvement.

You'veGot What It Takes If You Have...

Requirements:

• Bachelor's or master's degree in computer science, Software Engineering, Artificial Intelligence, Machine Learning, Data Science, or relevant experience.

• 3 years of comprehensive software engineering experience with at least 2+ years focused on AI/ML architecture, development, and deployment.

TechnicalExpertise:

• Proven track record in architecting and implementing enterprise-scale AI solutions using Microsoft Azure AI services and Generative AI technologies.
• Expert-level experience with Azure OpenAI Service, Azure AI Studio, Azure Machine Learning, and Azure
  Cognitive Services in production environments.
• Deep expertise in Gen AI application development, including advanced prompt engineering, RAG architectures, fine-tuning strategies, and multi-agent orchestration.
• Strong hands-on experience with Microsoft Copilot ecosystem, including enterprise Copilot deployment, custom copilot development, plugin architecture, and extensibility frameworks.
• Advanced proficiency in Python and/or C# with deep knowledge of AI/ML frameworks.
• Expert-level understanding of LLM architectures, embeddings, and vector search technologies.
• Expertise in designing and implementing scalable API architectures, microservices patterns, and event-driven systems for AI solutions.
• Deep understanding of Azure AI Search, vector databases (Cosmos DB, Pinecone, Weaviate), and hybrid search optimization techniques

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