Job description

We are seeking a Senior AI Engineer to design, implement, and support AI-driven document processing, retrieval, and search solutions. The ideal candidate will have expertise in Azure AI Search, Retrieval-Augmented Generation (RAG), Query Orchestration, and Kubernetes-based container deployments. This role requires deep technical skills in RAG-based architectures, Azure AI services, and on-premise open-source alternatives to drive cutting-edge AI solutions in document intelligence and retrieval. Key Responsibilities: • Architect and deploy AI-driven search and document intelligence solutions using Azure AI Search, Azure Document Intelligence, and RAG techniques. • Develop and optimize Query Orchestration strategies to efficiently route and structure user queries across multiple search and retrieval systems. • Implement and fine-tune RAG-based AI applications to enable intelligent knowledge retrieval from both structured and unstructured documents. • Deploy and manage containerized AI applications using Azure Kubernetes Service (AKS) for scalable processing. • Optimize vector search and embeddings pipelines to enhance AI-driven document retrieval. • Implement on-premise alternatives to Azure Document Intelligence using open-source solutions like Tesseract OCR, PyMuPDF, and Pillow. • Integrate with various APIs (e.g., Profile APIs, Product Metadata APIs, Download APIs) to enrich search capabilities and indexing processes. • Ensure compliance with export control restrictions and document handling best practices. • Monitor, troubleshoot, and optimize AI-based search, retrieval, and document processing workflows to ensure high performance. • Collaborate with stakeholders to define, implement, and refine AI-powered document solutions that meet business needs. Required Qualifications: • 5+ years of experience in AI/ML, cloud-based search, and document processing. • Expertise in Query Orchestration for handling complex AI search and retrieval pipelines. • Strong knowledge of RAG (Retrieval-Augmented Generation) architectures for AI-powered search. • Hands-on experience with Azure AI Search, Document Intelligence, and Cognitive Services. • Proficiency in vector search, embeddings, and hybrid search techniques. • Strong experience with Kubernetes (AKS) and containerized AI deployments. • Experience with on-premise document processing alternatives such as Tesseract OCR, PyMuPDF, and Pillow. • Proficiency in Python for developing AI pipelines and search systems. • Experience with Azure OpenAI, LangChain, or AI Foundry is a plus. Preferred Qualifications: • Experience in hybrid cloud AI solutions (on-prem + cloud). • Deep knowledge of Query Orchestration techniques for multi-index search optimization. • Expertise in vector databases and hybrid search architectures (e.g., FAISS, Weaviate, Pinecone). • Background in document classification, Natural Language Processing (NLP), and entity extraction. • Familiarity with export control restrictions and secure document handling best practices. Certifications: • No specific certifications required, though certifications in AI, cloud computing, or containerization would be beneficial. #J-18808-Ljbffr

Requirements