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.