Job Description

LLM Engineer - Generative AI

Responsibilities

Architect and enable distributed compute environments optimized for running large-scale language models (LLMs), aligning workloads with appropriate GPU resources (Small/Mid/High-end GPUs).

Leverage appropriate storage hardware and data formats to optimize data read/write efficiency, especially for large datasets used in model training and inference.Identify and resolve latency contributors, with a focus on I/O bottlenecks, inefficient data shuffling, and under/over-utilized compute resources.Scale LLMs by employing distributed training techniques, including data and model parallelism, to efficiently handle massive datasets.

Parallelize inference processing to minimize prediction latency.Provide subject matter expertise in the application of Graph and Vector databases for use cases such as Knowledge Graphs, Retrieval-Augmented Generation (RAG), and similar generative AI tasks.

Implement observability and monitoring solutions for LLM models, ensuring effective tracking of model performance, drift, and resource usage.Required Education and

Experience

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

Prior Experience With

Containerization technologies such as Docker and Kubernetes.Distributed systems and large-scale machine learning infrastructures.Databricks ML for managing machine learning workflows.Machine Learning Engineering, with a focus on deploying and scaling models.Cloud platforms (Azure preferred) for deploying and scaling LLM-based solutions.

Expert-level proficiency in Python and SQL for model development, data manipulation, and querying.

Preferred Qualifications:Expertise in fine-tuning large language models (LLMs), model evaluation, and prompt engineering for various generative AI applications.

Experience with LLM operations (LLMOps) for automating, monitoring, and managing model life cycles.

Familiarity with MosaicML or Ray Framework for distributed training and optimization of large models.Hands-on experience with LangChain, LlamaIndex, or similar frameworks for integrating LLMs into applications.

Experience with any vector database, such as Pinecone or Weaviate, for handling high-dimensional vector embeddings used in generative AI and search applications.

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