AI Engineer - Azure cloud

Role summary We are looking for an AI Engineer to design, build, integrate, and optimize AI-powered services and intelligent systems that enhance employee experience and support real-world HR technology use cases. The role is hands-on and engineering-focused, with emphasis on secure, scalable, measurable, and maintainable AI application delivery.      Role type Hands-on AI engineering resource Primary focus AI services, LLM integration, RAG patterns, prompt/context pipelines, evaluation, and Azure AI services Key responsibilities Design and develop AI-powered services that enhance employee experience and support HR technology use cases. Integrate and optimize large language models and intelligent systems using Azure OpenAI and other cloud-native AI tools. Apply advanced AI architecture patterns such as Retrieval-Augmented Generation (RAG), Agentic RAG, MCP, Function Calling, and A2A to practical enterprise use cases. Engineer robust pipelines for prompt design, context handling, embeddings, chunking strategies, and real-time data integration. Evaluate, test, and optimize model output and application performance to improve relevance, robustness, fairness, and explainability. Implement guardrails, prompt testing, adversarial and bias testing, and other controls needed for responsible AI application delivery. Develop and deploy cloud-based AI applications at scale using Azure Cloud Services for AI, including Azure OpenAI and Azure AI Search. Ensure solutions are secure, reliable, observable, maintainable, and well documented. Required skills and experience Excellent Python skills and hands-on experience Experience in AI application development, with focus on cloud-based AI model integration, deployment, and optimization. Experience with AI/ML and agentic application frameworks such as LangChain, LangGraph, Pydantic Proficiency in advanced AI architecture patterns, including RAG, Agentic RAG, MCP, Function Calling, and A2A, especially in an Azure environment Good understanding of GPT token usage, latency analytics, and budget guardrails. Sound understanding of AI guardrails, prompt fuzzing, adversarial testing, and bias testing. Experience in prompt engineering, context engineering, vector databases, embedding and chunking strategies, and real-time data integration. Experience evaluating model output and optimizing AI application performance. Hands-on experience with Azure Cloud Services for AI, including Azure OpenAI and Azure AI Search. Strong commitment to quality, maintainability, documentation, and continuous learning. Nice to have Understanding of alignment and feedback techniques, synthetic data generation, and continuous human-in-the-loop review loops. Experience designing evaluation approaches for relevance, groundedness, explainability, safety, robustness, and operational quality. Experience packaging AI features for production use with logging, monitoring, observability, and controlled rollout patterns.