Senior Lead AI Data Infrastructure Engineer

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 Client

Our 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) teama 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.

Responsibilities

• Platform 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.