AI Engineer

Job Summary:

Key Responsibilities

• AI Solution Development

  : Design, build, and deploy end-to-end intelligent applications and AI agents using Azure AI services, such as Azure OpenAI Service, Azure Machine Learning, Azure Cognitive Services (for vision, speech, and language), and Azure AI Search.

• Integration

  : Integrate AI models and capabilities into existing enterprise applications, data pipelines, and platforms like the Power Platform (Power Apps, Power Automate, Copilot Studio) using REST APIs, SDKs, and custom connectors.

• Model Implementation

  : Collaborate with data scientists to implement and train machine learning and deep learning models, manage their lifecycle (MLOps), and deploy them as scalable web services or container instances (e.g., Azure Kubernetes Service).

• Generative AI & LLMs

  : Design and implement generative AI solutions and conversational agents, utilizing prompt engineering, Retrieval-Augmented Generation (RAG) patterns, and knowledge mining to provide grounded responses.

• Collaboration & Documentation

  : Work closely with solution architects, data engineers, and business stakeholders to translate requirements into technical solutions. Document system designs, AI workflows, and operational guides.

• Monitoring & Optimization

  : Implement monitoring, logging, and performance tuning for AI applications using tools like Azure Monitor and Application Insights to ensure reliability and cost-efficiency.

• Security & Compliance

  : Apply Responsible AI principles and implement security best practices, including access control, data encryption, and content safety filters, to ensure compliance with industry standards.

Required Skills and Qualifications

• Experience

  : Typically requires 2-5+ years of experience in software or application development, with direct experience in building cloud-native and AI-enabled solutions.

• Programming Languages

  : Strong proficiency in Python, C#, Java, or JavaScript is essential for development and scripting.

• Azure Technologies

  : Hands-on experience with the Azure AI platform, including Azure Machine Learning, Azure Cognitive Services, Azure OpenAI, and related services like Azure Functions, Logic Apps, and Azure Cosmos DB.

• Development Practices

  : Experience with DevOps and MLOps practices, CI/CD pipelines (Azure DevOps/GitHub), containerization (Docker, Kubernetes), and API management.

• Problem-Solving

  : Excellent analytical and problem-solving skills to troubleshoot complex issues and optimize AI models and workflows.

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate (AI-102) or Microsoft Certified: Azure Developer Associate certifications are highly valued.