Senior AI Infrastructure Engineer, LLM/AI Platforms

Responsibilities

• Provision and configure large GPU clusters and compute resources for LLM training, finetuning, and inference workloads.
• Develop and optimize LLM model‑serving infrastructure, including deployment and optimization of various inference frameworks.
• Lead model lifecycle management including versioning, checkpointing and reproducibility across training and inference deployments.
• Design and champion robust evaluation frameworks to assess model performance, accuracy, and reliability, ensuring AI systems are consistently at production‑ready standards.
• Identify and address GPU utilization and GPU memory efficiency bottlenecks and apply techniques like quantization, batching, and caching.
• Architect and maintain data platforms and pipelines specifically designed to support LLMs, Retrieval‑Augmented Generation (RAG), and AI Agentic Systems at scale.
• Deliver production‑ready code with a focus on performance, maintainability, and testing rigor, ensuring the ability to ship fast without compromising quality.
• Apply expertise in data modeling, normalization, and semantic cataloging for AI/ML workloads.
• Define and enforce best practices for MLOps/Data Ops surrounding LLMs, including monitoring, observability, and zero‑touch recovery mechanisms for AI services.
• Document architectural designs thoroughly and communicate technical decisions clearly to stakeholders.
• Collaborate across the organization with Data Scientists, Product Managers, and other engineering teams to transform research prototypes into robust, production‑grade services.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, or a related STEM field; Master’s degree preferred.
• 6+ years of experience in Infrastructure/Data Engineering, with at least 2 years focused on building and maintaining platforms/pipelines that support LLM‑based systems and applications.
• Demonstrable hands‑on experience in LLM infrastructure engineering including cluster provisioning, optimizing training workloads, and maintaining inference pipelines.
• Exceptional ability to write clean, elegant, performant, and well‑tested code, coupled with a strong focus on action and delivering results quickly.
• Thorough understanding of engineering practices including effective peer code reviews and resilient architecture design.
• Demonstrates technical leadership and mentorship capabilities.
• Proven experience utilizing AI technologies to enhance decision‑making, streamline workflows and processes, improve efficiency and drive business outcomes.
• Core Competencies

Expertise in provisioning and optimizing GPU clusters for LLM training and inference, with a strong focus on MLOps best practices and robust model lifecycle management.

Proven ability to deliver high‑quality, production‑ready code while collaborating effectively with cross‑functional teams.

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