The RoleIn this role, the candidate will join a cutting‑edge engineering team focused on designing and implementing enterprise‑grade AI solutions using Azure AI Doc Intelligence, Azure OpenAI, and Azure AI Foundry. The position offers the opportunity to work with best‑in‑class Microsoft AI technologies to build intelligent automation capabilities, accelerate business processes, and elevate digital experiences across the organization.The Azure AI Engineer will be responsible for developing secure, scalable, and high‑performing AI solutions, including document intelligence workflows, generative AI applications, and retrieval‑augmented systems. This includes working on model orchestration, prompt engineering, document extraction pipelines, and system integrations with enterprise platforms such as CRM, ERP, and workflow automation tools.The role requires close collaboration with architects, product owners, data engineers, and cross‑functional teams to translate business requirements into technical AI solutions. Beyond development, the engineer will continuously monitor, evaluate, and optimize AI workloads, ensuring accuracy, compliance, and alignment with organizational standards.This is a hands‑on engineering role requiring strong problem‑solving abilities, in‑depth understanding of Azure AI capabilities, and a passion for driving innovation using large language models, and intelligent document processing.Your responsibilities: Design, develop, and deploy AI solutions using Azure AI Doc Intelligence, Azure OpenAI, and Azure AI Foundry. Build and operationalize LLM‑based applications, including prompt engineering, model customization, and grounding strategies. Develop intelligent document processing pipelines using Azure AI Doc Intelligence for tasks such as classification, extraction, OCR, and automated document workflows. Implement RAG (Retrieval‑Augmented Generation) architectures using Azure Search or vector databases. Integrate AI capabilities with enterprise systems through REST APIs, Azure Functions, Logic Apps, or Power Platform. Ensure adherence to security, data governance, and compliance standards, including data encryption, responsible AI, and regulated‑industry guidelines. Develop clean, maintainable, and well‑tested code following engineering best practices. Monitor AI model performance, optimize response quality, and proactively resolve bottlenecks or operational issues. Collaborate with business stakeholders to identify use cases, define solution architectures, and deliver high‑value AI outcomes. Produce architectural documentation, solution diagrams, and knowledge‑base content for ongoing support. Stay updated on emerging capabilities across Azure AI, LLMs, and enterprise AI technologies.Essential skills/knowledge/experience: 5–8 years of experience in software engineering or AI/ML development, with at least 4+ years working with Azure AI services. Strong hands‑on experience with Azure AI Doc Intelligence, including document extraction, form recognizers, classification, and OCR automation. Deep understanding of Azure OpenAI, including GPT models, prompt engineering, content filtering, and model tuning. Experience using Azure AI Foundry for building, managing, and operationalizing AI solutions. Practical experience architecting and implementing RAG‑based systems using Azure Search or vector index stores. Strong programming experience in Python, C#, JSON, or Node.js for integrating AI models and building backend services. Experience with Azure cloud services including Azure Functions, Logic Apps, Storage, App Services, Key Vault, and Event‑based architectures. Understanding of responsible AI practices, data privacy, compliance, and secure deployment models. Experience troubleshooting AI pipelines, optimizing cost/performance, and improving model accuracy. Hands‑on experience using Azure DevOps, Git repositories, CI/CD pipelines, and deployment automation. Strong analytical, communication, and stakeholder‑management skills.Desirable skills/knowledge/experience: Experience with vector databases such as Azure Cosmos DB (vector indexing). Familiarity with multi‑agent AI architectures, orchestration frameworks, or agentic workflows. Exposure to Power Platform AI features and AI Builder. Understanding of NLP concepts such as entity extraction, text analytics, embeddings, and conversation design.Certifications such as: Azure AI Engineer Associate (AI-102) Azure Fundamentals(AZ-900)
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