Data Science Jobs

#Job#Vacancy#AI#PerformanceEngineering#LLM#Remote Location: Remote (outside of Russia) Work format: Remote, Full-time Company name: CloudSquad Contacts: @natalia_kurland Job Title: Staff/Principal Performance Engineer About the Role: We are seeking a highly skilled and motivated Principal Performance Engineer to lead the performance optimization of our cutting-edge Generative AI technology stack. This role is critical n ensuring the scalability, efficiency, and reliability of our Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) systems. You will be a key driver in identifying and resolving performance bottlenecks, optimizing resource utilization, and ensuring a seamless user experience. You will work closely with our AI research, software engineering, and infrastructure teams to deliver world-class AI solutions. Responsibilities: 📌Performance Leadership: - Define and implement performance engineering strategies for our Generative AI full stack, including services, application, LLMs, RAG pipelines, and related infrastructure. - Lead performance testing, profiling, and analysis efforts to identify and resolve performance bottlenecks. - Establish and maintain performance benchmarks and SLAs for critical AI services. - Provide technical leadership and mentorship to performance engineering team members. 📌LLM Capacity and Tuning: - Analyze and improve LLM inference performance, including latency, throughput, and resource utilization. - Develop and implement strategies for LLM capacity planning and scaling. - Collaborate with AI researchers to evaluate and improve LLM model architectures and training techniques for performance. - Optimize LLM inference through techniques such as quantization, distillation, and optimized kernel implementation. 📌RAG Performance Optimization: - Design and implement performance tests for RAG pipelines, including retrieval, ranking, and generation components. - Identify and optimize performance bottlenecks in RAG systems, such as database queries, vector search, and document processing. - Evaluate and optimize RAG system architectures for scalability and efficiency. - Tune vector databases for optimal recall and latency. 📌Infrastructure Optimization: - Collaborate with infrastructure teams to optimize hardware and software configurations for AI workloads.● - Evaluate and recommend new technologies and tools for performance monitoring and analysis. - Develop and maintain performance dashboards and reports to track key metrics. - Optimize GPU utilization and memory management for LLM inference. 📌Collaboration and Communication: - Work closely with AI researchers, software engineers, and product managers to ensure performance requirements are met. - Communicate performance findings and recommendations to stakeholders at all levels. - Stay up-to-date with the latest developments in Generative AI and performance engineering. Qualifications: 📌Education: - Bachelor's degree in Computer Science, Engineering, or a related field (Master's preferred). 📌Experience: - 10+ years of experience in performance engineering, with a focus on large-scale distributed systems. - 2+ years of experience working with AI/ML technologies - Proven experience in performance testing, profiling, and analysis of complex software systems. - Deep understanding of NLP architectures, training, and inference. - Experience with vector databases and search technologies. - Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes). - Strong programming skills in python. - Experience with performance analysis tools (e.g., profilers, debuggers, monitoring tools). 📌Skills: - Strong analytical and problem-solving skills. - Excellent communication and collaboration skills. - Ability to work in a fast-paced and dynamic environment. - Passion for AI and a desire to push the boundaries of performance engineering