Senior AI Infrastructure Engineer, LLM/AI Platforms

Das ist der Job

Apply expertise in data modeling, normalization, and semantic cataloging for AI/ML workloads.

Darum lohnt es sich

Collaborate across the organization with Data Scientists, Product Managers, and other engineering teams to transform research prototypes into robust, production‑grade services.

Proven ability to deliver high‑quality, production‑ready code while collaborating effectively with cross‑functional teams. #J-18808-Ljbffr 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.

Define and enforce best practices for MLOps/DataOps surrounding LLMs, including monitoring, observability, and zero‑touch recovery mechanisms for AI services. Document architectural designs thoroughly and communicate technical decisions clearly to stakeholders.

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.