Job
Description
As a GenAI Engineer, you will be responsible for architecting and developing Generative AI-based applications using Azure OpenAI, Azure ML Studio, Azure Cognitive Search, and Azure Document Intelligence. Your role will involve building and optimizing Retrieval-Augmented Generation (RAG) pipelines for enterprise search, knowledge management, and domain-specific Q&A systems. You will integrate Large Language Models (LLMs) into production applications while ensuring performance, scalability, and governance. Designing intelligent workflows using LangChain, LlamaIndex, and Semantic Kernel for orchestrating multi-step AI interactions and agent behaviors will be a key aspect of your responsibilities. Collaboration with data scientists, ML engineers, and DevOps to deploy, monitor, and manage cloud-native AI solutions is essential. Additionally, you will incorporate OCR capabilities and document understanding pipelines using Azure Form Recognizer and Document Intelligence, ensuring compliance with enterprise standards, data privacy, and responsible AI principles. Leading proof-of-concepts (PoCs), rapid prototypes, and solution demos to validate GenAI use cases across departments is also part of your role. Maintaining a strong understanding of evolving LLM architectures, foundation models, and emerging GenAI techniques, as well as contributing to technical documentation, best practices, and reusable solution accelerators, are crucial aspects of the job. The ideal candidate for this position should hold a Bachelor's or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Proven experience with Azure AI services, including Azure OpenAI, Azure Machine Learning Studio, Azure Cognitive Search, and Azure Form Recognizer / Document Intelligence is required. Strong Python development skills, especially in the context of LLM pipelines and prompt engineering, are essential. Expertise with GenAI libraries and tools such as LangChain, LlamaIndex, and Semantic Kernel is highly desirable. Familiarity with RAG architecture, vector databases (e.g., FAISS, Redis, Azure Cognitive Search), and embedding models is a plus. Experience with containerization (Docker) and cloud-native deployment pipelines using Azure DevOps, Kubernetes, or Terraform is preferred. An understanding of Responsible AI principles, including bias mitigation, explainability, and model monitoring, is necessary. Excellent problem-solving and communication skills are also required, along with the ability to work in cross-functional agile teams. Preferred qualifications for this role include experience integrating agent-based frameworks for autonomous task execution and multi-agent collaboration, as well as knowledge of other GenAI ecosystems like Hugging Face Transformers, Anthropic Claude, or Mistral models. A background in enterprise content management, knowledge graphs, or intelligent document processing (IDP) would be beneficial. Azure certifications such as Microsoft Certified: Azure AI Engineer Associate and Microsoft Certified: Azure Solutions Architect Expert are a plus. In return, we offer you the opportunity to work on groundbreaking AI initiatives at an enterprise scale. You will be part of a collaborative, fast-paced, and innovation-driven team culture, with flexible work arrangements (remote/hybrid), competitive compensation, equity options, and comprehensive benefits.,
