Senior AI/Data Engineer - Cotton Holdings Inc.

📋 Description

• • Build and deploy AI-powered workflows to automate RFP intake, document parsing, extraction, and qualification processes across enterprise-scale document sets
• • Design and implement RAG (Retrieval-Augmented Generation) and semantic search pipelines specifically optimized for RFP documents to enable accurate opportunity qualification
• • Extract structured data elements including due dates, project scope, key contacts, technical requirements, and submission instructions from unstructured RFP documents using AI models
• • Develop and maintain FastAPI backend services with Pydantic and Pydantic AI for secure, scalable data ingestion and workflow orchestration
• • Create human-in-the-loop review and approval workflows to ensure quality control and auditability of AI-generated outputs
• • Engineer reliable data validation layers to enforce accuracy and consistency in extracted information across diverse document formats and sources
• • Integrate PostgreSQL with pgvector to store and query document embeddings for high-performance semantic search and retrieval
• • Utilize SQLAlchemy to define and manage relational data models for RFP metadata, extraction results, and workflow states
• • Deploy and manage containerized services using Docker in production environments with GitHub for version control and CI/CD pipelines
• • Implement observability, monitoring, and testing frameworks to ensure system reliability, detect model drift, and maintain AI output quality over time
• • Ingest and process RFP documents stored in Azure Blob Storage, applying preprocessing and normalization pipelines for consistent AI input
• • Work with Azure OpenAI models to generate embeddings and perform language understanding tasks tailored to procurement and contract analysis
• • Optimize AI workflows for latency, throughput, and cost efficiency without compromising data fidelity or compliance requirements
• • Collaborate with cross-functional teams to define success metrics for AI systems and translate business needs into technical specifications
• • Maintain clean, modular, and documented codebases that support long-term maintainability and onboarding of new engineers
• • Ensure all systems comply with organizational data governance policies and security standards for handling sensitive procurement information
• • Troubleshoot and resolve production issues related to document parsing errors, embedding mismatches, API failures, or workflow timeouts
• • Continuously iterate on system performance by analyzing user feedback, extraction accuracy metrics, and operational logs
• • Participate in code reviews and architectural discussions to uphold engineering standards across the AI platform team
• • Support the transition of systems from prototype to production-grade deployment with robust error handling and fallback mechanisms