Intercontinental Exchange
From Intercontinental Exchange's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Job Purpose
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 Engineer is responsible for the technical implementation, maintenance, and optimization of AI/ML infrastructure. This hands-on role focuses on GPU cluster deployment, container image management, platform tooling development, and deep technical troubleshooting. In addition, the engineer deploys and maintains AI-enabled workflow automation tools across LLM, MCP, and agentic capabilities, ensuring these systems operate efficiently and securely within a containerized architecture. This includes deploying and maintaining vector store infrastructure, implementing end-to-end RAG workflows, tuning agent memory systems, and hosting and managing MCP servers. The engineer also deploys and operates Agentic AI systems, including multi-agent orchestration frameworks and tool-use pipelines. The engineer serves as a core technical contributor on the AI Platform Operations team, translating architectural decisions into working infrastructure and enabling advanced, automated workflows across the platform.
Deploy, configure, and maintain GPU clusters and associated infrastructure
Designing, building, and maintaining the workflow automation platform that uses AI capabilities (LLM/MCP/Agentic capabilities)
Manage NVIDIA driver versions, CUDA toolkits, and container runtimes
Build and maintain approved container images with ML frameworks (PyTorch, TensorFlow, etc.)
Implement monitoring, alerting, and observability for GPU infrastructure
Deploy and maintain vector store infrastructure for RAG pipelines, agent memory, and semantic search
Implement and maintain end-to-end RAG workflows, including document ingestion, chunking, embedding generation, and retrieval optimization
Maintain and tune agent memory systems, including short-term context windows, long-term persistent memory stores, and episodic memory retrieval patterns
Deploy, operate, and maintain Agentic AI systems, including multi-agent orchestration frameworks and tool-use pipelines
Deploy, host, and maintain MCP servers within the containerized platform infrastructure
Manage MCP server configurations, versioning, access controls, and integration with agentic workflows
Monitor MCP server health, performance, and availability; respond to incidents and perform root cause analysis
Develop automation and tooling to improve platform reliability and efficiency
Provide L2/L3 technical support and vendor escalation for complex issues
Implement security controls including network policies, RBAC, and secrets management
Execute change requests and maintain technical documentation
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
Participate in a weekly on-call rotation for after-hours support
Knowledge and Experience
Bachelor's degree preferred.
3+ years in infrastructure engineering, systems administration, or DevOps
3+ years in scripting and automation skills (Python, Ansible, GitOps)
3+ years hands-on experience with Kubernetes in production
2+ years experience with Linux administration
Direct experience with GPU infrastructure (NVIDIA preferred)
1+ years experience using CUDA
1+ years experience using MCPs
1+ years experience with vector databases and embedding infrastructure
1+ years experience with RAG pipeline design and deployment
1+ years experience with agent memory patterns (in-context, external stores, retrieval-augmented memory)
1+ years experience with agentic AI systems using orchestration frameworks
1+ years experience with semantic search, embedding models, and ANN search techniques
1+ years working with workflow/orchestrion automation tools
Experience with enterprise monitoring and observability tools
Ability to work in a service-oriented team environment
Project Management, organization, and time management
Customer focused, and dedicated to the best possible user experience
Communicate effectively with both technical and business resources
Fluent speaking, reading, and writing in English
Desired Knowledge and Experience
1+ years of experience with AI developer toolkits (NVIDIA drivers, CUDA, cuDNN, and NCCL)
1+ years of experience with Run:AI, NVIDIA AI Enterprise, or DGX systems
1+ years of experience with n8n
1+ years of experience with GitHub Actions
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