Azure AI Engineer

Qualifications

• Bachelor’s degree in IT, Computer Science, or any related field.
• 3–6+ years in AI/ML, software engineering, or cloud-based AI solution development.
• Hands-on experience building and deploying AI/LLM-powered applications in production.
• Experience with Azure or similar cloud platforms (AWS/GCP).
• Proven work on prompt engineering, orchestration, or RAG-based solutions.
• Experience collaborating in cross-functional product or engineering teams.
• Knowledge in AI/LLM engineering: prompt design, orchestration (Prompt Flow), agent-based systems, and RAG implementation.
• Knowledge in Software engineering: Python, APIs (REST/JSON), microservices, and CI/CD practices.
• Knowledge in Azure AI ecosystem: AI Foundry, model deployment, inference APIs, and cost optimization.
• Knowledge in Data and search: embeddings, chunking strategies, and Azure AI Search (hybrid retrieval).
• Knowledge in Cloud and security: Azure networking, identity (Entra ID), observability, and secure-by-design architectures.
• Knowledge in Responsible AI: model safety, governance, explainability, and policy enforcement.
• Knowledge in Low-code integration: Copilot Studio and Power Platform extensibility.
• Strong analytical thinking and problem-solving in ambiguous environments.
• Effective collaboration across engineering, data, and product teams.
• Ability to define guardrails, KPIs, and success criteria for AI solutions

Job Description

• Design, build, and deploy AI models and end-to-end AI pipelines for production environments.
• Integrate AI capabilities into applications and services using APIs and cloud-native architectures.
• Collaborate with Data Engineers, Software Engineers, and Product Teams to ensure seamless data flow and system integration.
• Monitor, evaluate, and optimize model performance, accuracy, and scalability in real-world use.
• Develop and manage Prompt Flows, orchestration pipelines, and agent-based AI workflows.
• Implement Retrieval-Augmented Generation (RAG) solutions, including embedding, indexing, and context management.
• Ensure adherence to Responsible AI practices, including model safety, governance, and compliance standards.
• Establish observability, logging, and performance monitoring for AI systems.
• Apply secure-by-design principles, including identity management, access control, and data protection.
• Translate business requirements into AI solutions, defining guardrails, KPIs, and success criteria