Senior LLM AI Engineer
Pure StorageResponsibilities
- Design, build, and deploy LLM-based solutions for natural language interaction, search, analytics, and reasoning across structured, unstructured, and graph-based enterprise data.
- Implement data annotation, synthetic dataset generation, and model evaluation workflows.
- Lead fine-tuning and continuous improvement of proprietary LLMs to meet privacy, quality, and latency requirements.
- Define model training strategies and dataset requirements for model development and performance.
- Optimize LLM inference pipelines for latency, cost, throughput, and infrastructure efficiency.
- Deploy, operate, monitor, and improve LLM systems in production environments.
- Translate business challenges into LLM system designs and technical trade-offs.
- Guide architectural decisions and contribute to the technical direction of applied AI solutions.
Requirements
- Experience delivering AI-powered solutions into production and improving them over time.
- Hands-on experience developing retrieval and context orchestration pipelines combining structured and unstructured data for LLM grounding and reasoning.
- Experience implementing LLM training pipelines, including fine-tuning, instruction tuning, evaluation, and iterative model improvement.
- Proficiency in Python and modern LLM frameworks and APIs such as PyTorch, Hugging Face, Transformers, Gemini, and Claude.
- Experience with model optimization and deployment tooling such as ONNX, TensorRT-LLM, and vLLM.
- Experience collaborating with software engineers, product managers, and business stakeholders.
- Strong communication skills and the ability to explain machine learning concepts and system behavior clearly.
- Experience with semantic search, query understanding, Text-to-SQL, Text-to-Cypher, retrieval, or ranking systems is beneficial.
- Experience with agentic LLM frameworks for workflow orchestration, automation, and tool-based context augmentation is beneficial.
- Familiarity with MCP, Agent Skills, and LLM tool-calling frameworks is beneficial.
- Experience with LLM alignment techniques including RLHF and DPO is beneficial.
- Experience working with sensitive or privacy-constrained data and governance requirements is beneficial.
- Interest in exploring and applying advances in AI and LLM technologies is beneficial.
Benefits
- Primarily in-office work from the Prague office, except during PTO, work travel, or other approved leave.
- Flexible time off.
- Wellness resources.
- Company-sponsored team events.
- Accessibility accommodations are available throughout the hiring process.
Categories
About Pure Storage
Pure Storage (NYSE: PSTG) uncomplicates data storage, forever. Pure delivers a cloud experience that empowers every organization to get the most from their data while reducing the complexity and expense of managing the infrastructure behind it. Pure’s commitment to providing true storage as-a-service gives customers the agility to meet changing data needs at speed and scale, whether they are deploying traditional workloads, modern applications, containers, or more. Pure believes it can make a significant impact in reducing data center emissions worldwide through its environmental sustainability efforts, including designing products and solutions that enable customers to reduce their carbon and energy footprint. And with a certified customer satisfaction score in the top one percent of B2B companies, Pure's ever-expanding list of customers are among the happiest in the world. For more information, visit www.purestorage.com.