We are seeking a highly senior, hands-on AI Data Infrastructure Engineer (potentially at a Lead level) to architect and own our institutional AI foundation. This is a specialized role at the intersection of Data Engineering and Software Engineering.Unlike a traditional AI Developer, your focus will be on the infrastructure, tooling, and ecosystem that powers AI, rather than building individual end-user solutions. You will modernize our data environment, making it 'AI-ready,' and ensure our platform is robust, scalable, and cost-optimized to support the next generation of online education and healthcare simulations.About the ClientOur customer is a leader in online education, dedicated to empowering professionals through innovative simulation and learning platforms. We are a certified great workplace, ranked consistently by Fortune as a top employer for Millennials and Women.You will join the Data, AI & Automation (DAIA) team—a tight-knit, remote-first group of passionate experts driven by curiosity. We work in a fast-paced environment where we value 'human-centric' AI that solves real-world problems in the healthcare and allied health fields.ResponsibilitiesPlatform Ownership: Set up, maintain, and own the core AI platform infrastructure with a primary focus on Snowflake Cortex and its surrounding ecosystem.Infrastructure as Code & Tooling: Configure and maintain MCP (Model Context Protocol) servers and manage the integration of open-source packages (e.g., Goose).Cost & Performance Optimization: Actively manage Snowflake credits, token usage, and overall system performance to ensure a cost-effective and resilient environment.Data Architecture: Modernize and refine high-level platform architecture, ensuring external datasets are seamlessly integrated and 'AI-ready.'Observability: Implement and maintain high standards for system monitoring, observability, and reliability.Technical Leadership: Act as a self-starting, independent lead who can translate high-level infrastructure needs into functional, production-grade code.Must-Have:Expert-level Snowflake: Extensive hands-on experience with Cortex, including setup, management, and cost optimization.Snowflake Suite: Deep expertise (SME) in Snowpark and Streamlit.Programming: Advanced proficiency in Python and a strong background in Software Engineering.AI Infrastructure: Proven experience in MCP (Model Context Protocol) server development and configuration.Cloud & Data: Deep understanding of data modeling, data architecture, and AWS environments (specifically AWS Bedrock).Proficiency in core AWS infrastructure: S3 (data lakes), IAM (permissions/security), Lambda (serverless compute), and VPC/Networking (secure cloud connectivity).Seniority: Minimum 5+ years of experience in data/infrastructure engineering, showing the ability to work independently and interface directly with internal technical stakeholders.Nice-to-Have:GenAI/RAG: Practical experience deploying Generative AI and Retrieval-Augmented Generation (RAG) systems in a production setting.Machine Learning: A background in ML engineering or MLOps (e.g., experience with AWS SageMaker).Open Source: Experience contributing to or managing open-source AI tooling like Goose.