What are the responsibilities and job description for the Sr Search and AI Engineer position at Careers Integrated Resources Inc?
Job Summary
We are seeking a Senior AI Engineer to design, implement, and support AI-driven document processing, retrieval, and search solutions using Azure AI Search, Retrieval-Augmented Generation (RAG), Query Orchestration, and Kubernetes-based container deployments. The ideal candidate will have expertise in RAG-based architectures, Azure AI services, and on-premise open source alternatives.
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 route and structure user queries efficiently across multiple search and retrieval systems.
- Implement and fine-tune RAG-based AI applications for intelligent knowledge retrieval from 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, leveraging open-source solutions (e.g., Tesseract OCR, PyMuPDF, pillow).
- Integrate with APIs (e.g., Profile APIs, Product Metadata APIs, Download APIs) to enrich search capabilities and indexing processes.
- Ensure compliance with export control restrictions and secure document handling best practices.
- Monitor, troubleshoot, and optimize AI-based search, retrieval, and document processing workflows.
- Collaborate with stakeholders to define, implement, and refine AI-powered document solutions.
- 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.
- Experience with Azure AI Search, Document Intelligence, and Cognitive Services.
- Hands-on experience with 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, pillow.
- Proficiency in Python for AI pipeline and search system development.
- 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, NLP, and entity extraction.
- Familiarity with export control restrictions and secure document handling best practices.
