Job Purpose

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.

Responsibilities

• 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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