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What you’ll be doing:
Build GPU accelerated scalable LLM driven Retrieval Augmented Generation(RAG) workflow and build a scalable microservice based architecture deployable on multi-node, multi-cloud environment
Build domain specific agents and workflows and build a framework which can support multi-turn, multi-modal, multi-user conversations with a LLM driven agents.
Develop knowledge discovery, and reasoning capabilities including but not limited to disambiguation, clarification, and anticipation for dialogue systems
Evaluate and benchmark end to end RAG and conversational AI agent pipelines for accuracy as well as system performance
Analyze RAG and conversational AI agent end to end accuracy and limitations and recommend the next course of action & Improvements.
Characterize performance and quality metrics across platforms for various AI and system components
Collaborate with various teams on new product features and improvements of existing products. Customize and integrate the conversational AI framework with other NVIDIA products
Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.
What we need to see:
Bachelor's degree or Master’s degree (or equivalent experience) in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math
5+ years of experience and excellent programming skills in Python
Knowledge of Large Language model applications
Familiarity with microservices, Docker, helm, kubernetes etc.
Experience of working on end to end Software lifecycle, release packaging & CI/CD pipeline
Hands-on experience on conversational AI Technologies like Large Language Models, Information Retrieval, Natural Language Processing, Dialogue systems (including system integration, state tracking and action prediction), Question and Answering, etc.
Knowledge of vector databases and embedding models
General background around version control and code review tools like Git, Gerrit, Gitlab.
Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic environment
Ways to stand out from the crowd:
Strong fundamentals in Programming, optimizations and Software design
Strong knowledge of ML/DL techniques, algorithms and tools with exposure to CNN, RNN (LSTM), Transformers (BERT, GPT, Megatron), Language Models
Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT
Background with deploying machine learning models on data center, cloud, and embedded systems
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