Compunnel, Inc.
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.
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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.
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