Lead Engineer, Platform Engineering - AI. Jacksonville Move Collective

Overview

We are on a mission as a team. We are problem solvers and partners, always starting with our customers to solve their challenges and create opportunities. Our start-up roots keep us nimble, flexible, and moving fast. We take ownership and make decisions. We all work for one company and work together to drive growth across the business. We engage in robust debates to find the best path, and then we move forward as one team.

We take pride in what we do, acting with integrity and passion, so that our customers can perform better. We are experts and enthusiasts - combining ever-expanding knowledge with leading technology to consistently deliver results, solutions and opportunities for our customers and stakeholders. Every day we work toward transforming global markets.

The AI Platform Engineering Lead drives the AI Platform Operations team, guiding platform strategy, governance, and stakeholder engagement. They align technical execution with business goals, ensuring cost‑effective, secure, and scalable AI/ML solutions. The lead defines the architecture, standards, and governance across AI/ML infrastructure, workflow automation, and agentic AI capabilities, including RAG pipelines, vector store infrastructure, agent memory frameworks, and MCP server strategy.

They establish design patterns and best practices that span LLM, MCP, and agentic capabilities, ensuring the platform scales securely and operates with consistency across the enterprise. The lead collaborates across teams to set security standards, manage resources, maintain compliance, and align platform capabilities with enterprise architecture standards.

• Define platform strategy, roadmap, and capability evolution
• Establish governance frameworks, policies, and exception processes
• Manage team budget, Cap Ex planning, and vendor relationships
• Build and lead the AI Platform Operations team
• Define the architecture, standards, and governance for AI/ML infrastructure, including GPU cluster design, compute resource planning, security controls, and observability across the platform
• Drive the strategy, standards, and governance for AI‑enabled workflow automation across LLM, MCP, and agentic capabilities, ensuring the platform scales securely and operates with consistency across the enterprise
• Define the architecture and design standards for vector store infrastructure supporting RAG pipelines, agent memory, and semantic search across the enterprise
• Establish design patterns and best practices for RAG workflow implementation, including ingestion strategies, chunking approaches, embedding model selection, and retrieval optimization
• Architect agent memory frameworks, defining standards for short‑term context, long‑term persistent memory, and episodic memory patterns across AI platform workloads
• Drive the architecture and governance of Agentic AI systems, including multi‑agent orchestration design and tool‑use pipeline standards
• Define the strategy and architecture for hosting and managing MCP servers across the platform, including deployment topology, security boundaries, and integration standards
• Establish governance frameworks and policies for MCP server lifecycle management, versioning, and access control
• Evaluate and select MCP server tooling and vendors; manage relationships and roadmap alignment
• Serve as executive liaison for platform matters
• Own major incident management and executive communication
• Drive continuous improvement and platform maturity initiatives
• Align platform capabilities with enterprise architecture standards
• Respond to and assist in production operations in a 24/7 environment
• Provide technical analysis, resolve problems, and propose solutions
• Provide support to, and coordinate with, developers, operations staff, release engineers, and end‑users
• Educate and mentor team members and operations staff

• 8+ years in IT infrastructure or platform engineering roles
• 3+ years in technical leadership or management positions
• 1+ years hands‑on experience with Kubernetes in production
• Direct experience with GPU infrastructure (NVIDIA preferred)
• 2+ years experience using CUDA
• 1+ years experience using MCPs
• 2+…