Certified Instructor Program

Practical Experience:

Candidates must demonstrate hands‑on experience deploying and optimizing large language model (LLM) inference systems on GPUs in a production or realistic lab environment. 

Qualifying experience includes:

• A professional role (e.g., ML engineer, MLOps engineer, platform engineer) responsible for operating LLM or other deep learning inference services, including capacity planning, latency/throughput tuning, and cost optimization.
• Academic or industrial lab work involving Kubernetes‑based deployment of GPU workloads, including use of Helm charts, GPU operators, and autoscaling for AI services.

Knowledge and Expertise:

 Candidates should also have the following:

• Solid understanding of LLM lifecycle and scaling regimes
• Deep familiarity with transformer inference internals (e.g. prefill vs. decode phases, KV cache mechanics, etc.)
• Knowledge of inference optimization techniques
• Experience comparing managed AI services with open‑source/self‑hosted solutions
• Working knowledge of Kubernetes including GPU enablement and networking
• Experience with at least one of: NVIDIA NIM microservices, vLLM, or similar self‑hosted LLM stacks
• Understanding of inference‑time controls
• Experience generating and analyzing parallel traffic to LLM endpoints
• Fluency in numeric precision and hardware utilization trade‑offs for LLMs
• Familiarity with benchmarking and performance tools such as AIPerf or equivalents
• Knowledge of LLM‑aware gateways (e.g., Envoy AI Gateway)
• Experience with autoscaling GPU inference services using metric‑driven mechanisms such as KEDA
• Familiarity with GPU monitoring stacks
• Understanding of disaggregated LLM serving
• Awareness of advanced orchestration patterns for tightly coupled AI workloads

Practical Experience:

Candidates must demonstrate hands-on experience building, configuring, and evaluating multi-agent AI systems that integrate LLMs with domain-specific tooling, preferably in an industrial, IoT, or predictive maintenance context. 

Qualifying experience includes:

• A professional role (e.g., AI/ML engineer, data scientist, applied AI researcher, solutions architect) where they have designed or operated agentic AI workflows that orchestrate multiple LLM calls, custom tool invocations, and multi-step reasoning chains to solve real-world problems.
• Academic or industrial project work involving predictive maintenance, asset health monitoring, or reliability engineering using sensor time-series data, including building data pipelines, training, or deploying ML models for remaining useful life (RUL) estimation, and generating actionable maintenance recommendations.

Knowledge and Experience:

Candidates should also have the following:

• Solid understanding of agentic AI concepts such as ReAct agents, Chain-of-Thought reasoning, and multi-agent orchestration (planner/executor patterns)
• Experience with NVIDIA NeMo Agent Toolkit (NAT) or similar agentic frameworks (e.g., LangChain, LlamaIndex agents, AutoGen) including config-driven agent definition, tool registration, and prompt engineering
• Familiarity with NVIDIA NIM microservices or equivalent experience deploying and invoking LLM inference endpoints
• Experience building and registering custom tools for LLM agents, including input schema definition and system prompt updates to guide tool usage
• Working knowledge of LLM observability and tracing tools such as Phoenix, LangSmith, or OpenTelemetry-based solutions for debugging agent behavior and monitoring token usage and latency
• Experience with evaluation frameworks for agentic AI, including designing eval datasets, running automated eval harnesses, and interpreting scoring results
• Familiarity with LLM-driven code generation and sandboxed execution, including security considerations such as Docker-based isolation and library allowlisting
• Understanding of time-series foundation models (e.g., MOMENT, NV-Tesseract) for anomaly detection and how they compare to traditional statistical and ML-based approaches
• Practical experience with anomaly detection on sensor or industrial time-series data, including correlating anomaly patterns with asset degradation and remaining useful life
• Working knowledge of SQL and text-to-SQL integration within agent tool pipelines
• Comfortable developing in Python/Jupyter with proficiency in pandas, NumPy, and visualization libraries (Plotly, Matplotlib, Seaborn)
• Familiarity with predictive maintenance concepts including asset lifecycle stages, failure modes, degradation patterns, and sensor-based health monitoring

Practical Experience: 
Candidates must demonstrate significant prior experience working with NVIDIA CUDA/GPU-accelerated applications, either in a professional or meaningful academic scenario, and should be able to discuss their work, related to the following:

• How meaningful acceleration was achieved on a problem that could not be addressed as successfully in a CPU-only environment
• Details about the applied strategies the applications use in relation to a GPU architecture
• Technical challenges encountered, tailored to CUDA-specifics, and how they were addressed

Knowledge and Expertise:
Candidates should have the following:

• Basic understanding of computer architecture (memory hierarchies, computing cores, etc.)
• Foundational knowledge in parallel computing
• Awareness of race conditions and familiarity with methods to prevent them
• An understanding of synchronization mechanisms between threads/processes
• Medium to advanced knowledge and experience in modern C++ programming, including understanding classes, functors, and Lambda functions
• Knowledge and experience with the C++ Standard Template Library (STL), including extensive use of iterators

Practical Experience:

Candidates must demonstrate hands-on experience with GPU‑accelerated numerical computing or HPC using Python, including work with libraries such as NumPy, CuPy, and preferably Numba or CUDA C/C++ in either a professional or academic setting. 

Qualifying experience includes:

• A professional role (e.g., ML/AI engineer, HPC engineer, data engineer, software engineer) where they have implemented and optimized array‑based computations on GPUs (e.g., linear algebra, reductions, scans, sorting, histogramming) using tools like CuPy, cuda‑cccl, or custom CUDA kernels.
• Academic or research projects involving GPU programming concepts such as memory spaces, data transfer optimization, CUDA streams, asynchronous execution, and performance profiling with Nsight/NVTX or similar tools.

Knowledge and Expertise:

 Candidates should also have the following:

• Strong array‑programming skills with the NumPy API (vectorization, broadcasting, linear algebra, reductions, sorting) and an ability to reason about performance and data movement.
• Practical experience using CuPy as a NumPy drop‑in for GPU acceleration, including benchmarking and understanding key API differences.
• Working knowledge of core CUDA concepts: host vs device memory, transfers, streams, synchronization, and asynchronous execution from Python.
• Experience with CUDA Core Compute Libraries (cuda‑cccl) for reductions, scans, transforms, and GPU sorting, plus familiarity with custom GPU kernels (Numba CUDA or similar) using shared memory and atomics.
• Comfortable developing in Python/Jupyter (or Colab), organizing notebooks, and using basic profiling and visualization tools.

Practical Experience: 
Candidates must demonstrate experience working on a computer vision task—image classification, object detection, etc.—using deep learning in either a professional or academic setting. Foundational knowledge of natural language processing (NLP), reinforcement learning (RL), and other neural network architectures, such as RNNs/LSTMs and GANs, is required. Qualifying experience includes:

• A professional role (e.g., data engineer, data scientist) architecting computer vision projects that use deep learning
• Academic coursework in computer vision, NLP, RL, and neural network architectures

Knowledge and Expertise:
Candidates should also have the following:

• Familiarity with basic programming fundamentals, such as functions and variables
• Basic Python competency

Practical Experience: 
Candidates must demonstrate experience working on at least one natural language processing (NLP) application using a transformer-based architecture (such as BERT), either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

• A professional role (e.g., engineer, data scientist) on an NLP project that used a transformer-based architecture
• A completed NLP project that used a transformer-based architecture
• Academic coursework in NLP transformer-based networks

Knowledge and Expertise:
Candidates should also have the following:

• Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, array manipulations, and class objects/methods
• Basic pandas and NVIDIA NeMo competency
• Experience using NVIDIA Triton Inference Server

Practical Experience: 
Candidates must demonstrate significant experience with data science in Python using distributed computing for large datasets and should be able to discuss the following about their work:

• Specifics about all aspects of their end-to-end workflows, including explaining their decisions and speaking knowledgeably about tools and libraries used
• The use of various data transformations applied on input data for model consumption
• The use of various machine learning algorithms in their work and explain their decisions
• Extensive use of Python data science libraries like pandas, NumPy, scikit-learn, and xgboost
• Previous work with or on NVIDIA RAPIDS and Dask
• Recognition of the iterative nature of data science and appreciation of hardware acceleration for rapid experimentation

Knowledge and Expertise:
Candidates should have the following:

• Experience with Python and common data science libraries like pandas, NumPy, scikit-learn, and xgboost
• Proficiency with DataFrame manipulation
• Familiarity with distributed computing using Dask
• Familiarity with end-to-end machine learning workflow
• Proficiency with various machine learning models, specifically those of tree-based variant
• Proficiency with model performance metrics, such as accuracy and inference performance
• Familiarity with model tuning and its benefits
• Knowledge of NVIDIA’s RAPIDS, NVTabular, and Triton Inference Server

Practical Experience: 
Candidates must demonstrate significant experience with Data Science in Python and should be able to discuss about their previous work:

• Specifics about all aspects of their end-to-end workflows, explaining their decisions, and speaking knowledgeably about tools and libraries used
• The use of many DS/ML algorithms in their work, explaining their decisions
• Extensive use of Python DS libraries like Pandas, NumPy, scikit-learn, NetworkX
• Encouraged, previous work with Dask. Polars, and/or RAPIDS

Knowledge and Expertise:
Candidates should have the following:

• Classroom teaching experience
• Significant presentation experience

Practical Experience: 
Candidates must demonstrate significant experience in data science, machine learning, deep learning, and the telecommunications industry. They should have worked on at least one significant AI application, either in a commercial or academic capacity, and be able to explain their work. Qualifying experience includes:

• A role as a major contributor to a project that used deep learning
• A role as a major contributor to a project that used other machine learning techniques
• A role as a major contributor to a project that required data science

Knowledge and Expertise:
Candidates should also have the following:

• Professional data science experience using Python
• A working understanding of NVIDIA RAPIDS
• Significant experience in machine and deep learning, specifically XG Boost, AutoEncoder, and GAN models
• Exposure to the telecommunications industry and cybersecurity, specifically networking and the threat of network intrusion

Practical Experience: 
Candidates must demonstrate experience working on at least one deep learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

• Using deep learning techniques to tackle classification problems, preferably in the context of industrial applications
• A professional role on a computer vision project that used deep learning techniques
• Significant coursework in deep learning for computer vision that covers the various stages of the development workflow

Knowledge and Expertise:
Candidate should have: 

• Knowledge of Python and common Python libraries used in deep learning (e.g., numpy and pandas)
• Familiarity with end-to-end machine learning workflow
• Familiarity with manipulating data using pandas DataFrame
• Familiarity with deep learning concepts, including knowledge of convolutional neural networks
• Familiarity with at least one deep learning framework (Keras and TensorFlow are preferred)
• Familiarity with metrics, such as accuracy and inference performance
• Familiarity with command-line interface and basic Linux commands
• Familiarity with transfer learning and fine-tuning models
• Knowledge of NVIDIA’s DALI, TAO Toolkit, TensorRT, and Triton Inference Server

Practical Experience: 
Candidates must demonstrate experience working on at least one deep learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

• Deploying deep learning training workloads to multiple GPUs and preferably multi-node clusters
• Data parallel approaches to distributed deep learning
• Profiling and optimizing the deep learning code
• Using NVIDIA NGC containers
• Experience in building neural networks with PyTorch
• Using PyTorch DDP to deploy distributed training

Knowledge and Expertise:
Candidate should have the following:

• Strong grasp of the literature discussing implications of training deep neural networks with large batches—in particular, a good understanding of the LARS/LARC algorithm
• Knowledge of the process used in training deep neural networks—In particular, understanding of the Stochastic Gradient Descent and backpropagation algorithms

Practical Experience: 
Candidates must demonstrate experience working on a model-parallelism-related task using deep learning in either a professional or academic setting. Foundational knowledge of optimization techniques, such as activation checkpointing, mixed precision training, and gradient accumulation, is required. Qualifying experience includes:

• A professional role (e.g., data engineer, data scientist) architecting deep learning projects that use distributed systems like the cloud or multi-GPU machines
• Academic coursework in large neural network architectures, such as GPT-3

Knowledge and Expertise:
Candidates must also demonstrate: 

• An understanding of the Slurm, NVIDIA Triton, and DeepSpeed technologies
• An understanding of the differences between model and data parallelism

Practical Experience: 
Candidates must demonstrate experience working on at least one LLM application involving fine-tuning, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:

• How their applications provide meaningful acceleration on a problem that could not be addressed as successfully in a CPU-only environment
• The specifics of optimization strategies that the applications use
• Specific NVIDIA CUDA-related technical challenges that arose while developing the applications

Knowledge and Expertise:
Candidates should have expertise in the following areas:

• Differentiating RAG, fine-tuning, and alignment
• Strategies to drive the creation of diverse synthetic data sets
• Parameter-efficient fine-tuning
• Pruning techniques
• Distillation techniques
• Large language model (LLM) decoding strategies, such as top-k/p and beam search.
• LLM output evaluation techniques, such as ROUGE/BLEU, semantic similarity, and LLM-as-a-judge

Practical Experience: 
Candidates must demonstrate experience working on at least one agentic AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:

• A professional role (e.g., engineer, data scientist)
• A completed project
• Academic coursework

Knowledge and Expertise:
Candidates should have experience with the following:

• Modern LangChain, including LCEL, LangGraph, etc.
• Differentiating capabilities of various agentic tools, such as LangGraph, CrewAI, and Autogen
• Stateful large language model (LLM) systems
• LLM tool calling
• Strategies to prevent derailing
• Agentic routing

Practical Experience: 
Candidates must demonstrate experience working on at least one large language model (LLM) application using a programmatic interface, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:

• A professional role (e.g., engineer, data scientist)
• A completed project
• Academic coursework

Knowledge and Expertise:
Candidates should have experience with the following:

• Development in Python, including an understanding of Pydantic
• Modern LangChain, including LCEL, LangGraph, etc.
• LLM next token prediction decoding methods 
• How LLM models are developed (pretraining, alignment, instruction-tuning, etc.)
• LLM prompting techniques, including iterative, zero/one/few-shot, chain of thought.
• Agents using tools, such as ReAct

Practical Experience: 
Candidates must demonstrate experience working on at least one generative AI application incorporating inputs of multiple modalities, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:

• A professional role (e.g., engineer, data scientist)
• A completed project
• Academic coursework

Knowledge and Expertise:
Candidates should have experience with the following:

• Details of implementing contrastive pretraining
• Techniques for combining embeddings of various modalities, such as vision-language model projections
• Tools for chunking documents, including text, titles, figures, charts, and tables
• Graph retrieval-augmented generation (RAG) and associated technology, such as knowledge bases and cypher queries

Practical Experience: 
Candidates must demonstrate significant experience in data science, machine learning, deep learning, and the telecommunications industry. They must have worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

• Active open-source contribution or coordination efforts in the area
• Experience orchestrating dialog management and information retrieval systems
• Strong applied software engineering expertise, especially surrounding microservices and inference server solutions

Knowledge and Expertise:
Candidates should have the following:

• Strong proficiency in Python, including functional programming and server deployment
• Expertise in large language models as inference endpoints, including industry use cases
• Strong experience with modern LangChain (including LCEL) and LangServe required; understanding of LangGraph, LlamaIndex, Langsmith, and NeMo Guardrails useful
• Experience with microservice/server orchestration, including Docker and FastAPI
• Experience with modern retrieval-augmented generation (RAG), including some derivative formulations and pros/cons
• Understanding of agentic behavior, tooling, and modular agent components
• Intuition of evaluation metrics and performance expectations

Practical Experience: 
Candidates must demonstrate significant experience in deployment of retrieval systems. They must have worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

• Active open-source contribution or coordination efforts in the area
• Experience orchestrating dialog management and information retrieval systems
• Strong applied software engineering expertise, especially surrounding microservices and inference server solutions

Knowledge and Expertise:
Candidates should have experience with the following:

• Strong proficiency in Python, including functional programming and server deployment
• Expertise in large language models as inference endpoints, including industry use cases
• Experience with microservice/server orchestration, including Docker and FastAPI
• Retrieval systems, including combination of embedders and rerankers
• Intuition of evaluation metrics and performance expectations
• Container orchestration platforms, especially Kubernetes clusters
• Monitoring tools like Prometheus

Practical Experience: 
Candidates must demonstrate thorough, up-to-date experience with deep learning, computer vision, and diffusion models. Ideal candidates should have background knowledge of surrounding material, as well as active roles that expose them to the latest trends, innovations, and emerging intuitions. Qualifying experiences include:

• A professional role (e.g., machine learning engineer, data scientist) architecting deep learning projects that generate images
• Active open-source contribution or coordination efforts in the area
• Academic coursework in using AI to generate images

Knowledge and Expertise:
Candidates should have the following:

• Proficiency in Python and PyTorch
• Active intuitive understanding of CLIP and multimodal AEs/VAEs/GANs/Stable Diffusion
• Intuitive understanding of audio/video/image classification/captioning/transcription
• Foundation in statistics, including the normal distribution and random sampling

Practical Experience: 
Candidates must demonstrate thorough, up-to-date experience with deep learning, large language models, and agent systems. Ideal candidates should have background knowledge of surrounding material, as well as active roles that expose them to the latest trends, innovations, and emerging intuitions. Qualifying experiences include:

• Chat model/multimodal model architecture design experience
• Experience with the training loop and pipeline assumptions/intuitions
• Active open-source contribution or coordination efforts in the area
• Experience orchestrating dialog management and information retrieval systems

Knowledge and Expertise:
Candidates should have the following:

• Advanced proficiency with Python, sufficient for reading Hugging Face source code
• Hugging Face comfort, including serialization, model release, HF Transformers, etc.
• Experience designing systems with LLM constituent components
• Familiarity with PyTorch, deep learning, generative AI, multimodal models, etc.
• Understanding of experimentation/deployment with LLM systems, including hardware requirements, safety considerations, and evaluation techniques.
• Intuitive understanding of audio/video/image classification/captioning/transcription
• Active intuitive understanding of CLIP and multimodal AEs/VAEs/GANs/Stable Diffusion
• LangChain experience, including intuitions and details of current developments
• Familiarity with retrieval-augmented generation (RAG), including LlamaIndex, VDB services, retriever models, and more
• Comfort with NVIDIA value propositions surrounding LLMs, RAG, NVIDIA NeMo, etc.

Practical Experience: 
Candidates must demonstrate experience working on at least one robotics simulation project, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:

• A professional role (e.g., engineer, data scientist)
• A completed project
• Academic coursework

Knowledge and Expertise:
Candidates should have experience with the following:

• NVIDIA Isaac Sim
• OpenUSD
• URDF models
• ROS2
• Robotic navigation, i.e., SLAM
• Synthetic data generation

Candidates must demonstrate comprehensive, up-to-date expertise in data center networking. Ideal candidates have hands-on expertise in advanced AI networking technologies and real-time monitoring.

Practical Experience: 

• Professional experience (e.g., network engineer, system administrator, infrastructure engineer, solutions architect, DevOps, trainers) deploying, configuring, and managing Cumulus Linux-based network environments in production data centers.
• Experience with NVIDIA Air cluster simulation and deployment.

Knowledge and Expertise:

• Proficiency in Linux administration (shell, config management, troubleshooting) 

• Strong knowledge of Ethernet networking, switching, and routing 

• NVIDIA networking hardware experience preferred 

• Layer 2 and Layer 3 networking: VLANs, bridging, trunking, link aggregation (LAG/MLAG), SVIs, VRR, VRF, and BGP (including BGP unnumbered)

• Network virtualization using VXLAN and EVPN, including both symmetric and asymmetric routing models 

• Experience with network automation tools and workflows (e.g., Ansible, REST APIs, Zero Touch Provisioning)

• Monitoring, diagnostics, and troubleshooting across multiple network layers, including hardware resource monitoring and OpenTelemetry 

Preferred: 

• NVIDIA Cumulus Linux course certificate or NCP-AIN certification (NVIDIA Certified Professional AI Networking)

• Active involvement in open-source networking projects or community

Candidates must demonstrate comprehensive, up-to-date expertise in data center networking. Ideal candidates have hands-on expertise in advanced AI networking technologies and real-time monitoring. 

Practical Experience: 

• Professional roles such as network engineer, DevOps, technical instructors, or system administrators working with Spectrum-X, Cumulus Linux, and AI data center networks 

• Cumulus Linux Certified Instructor 

• Experience with NVIDIA Air cluster simulation and deployment 

• Real-time monitoring and troubleshooting with NVIDIA NetQ, Cumulus Linux CLI, and telemetry tools (ASIC, OTLP, DTS) 

Knowledge and Expertise:

• Cumulus Linux: Hands-on experience with installation, configuration, upgrades, Layer 2/3 features, network virtualization (VXLAN/EVPN), automation, and troubleshooting 
• Networking: Strong knowledge of Ethernet, switching, routing, and data center networking automation 

• Linux: Proficiency in Linux administration and managing Linux-based network environments 

Preferred:

• NVIDIA Cumulus Linux course certificate or NCP-AIN certification (NVIDIA Certified Professional AI Networking)

• Active involvement in open-source networking projects or community

Candidates must demonstrate comprehensive, up-to-date expertise in deploying and managing AI data center infrastructure, including compute, networking, storage, and virtualization. Ideal candidates have hands-on experience with advanced AI infrastructure technologies and workflows. 

Practical Experience: 

• Professional roles such as data center administrator, DevOps engineer, system administrator, or AI infrastructure engineer working with enterprise-scale AI environments
• Direct, practical experience with: 
• Deploying and managing AI compute platforms (GPUs, CPUs, DPUs) 
• Storage architecture and performance optimization for AI data centers 
• Virtualization technologies (VMs, containers, GPU partitioning with vGPU/MIG) 
• Installing and managing NVIDIA software (GPU drivers, DOCA, NVIDIA NGC containers, NVIDIA AI Enterprise Suite) 
• Using management tools such as NVIDIA Base Command Manager (BCM) for AI cluster provisioning and operations 

Knowledge and Expertise:

• Networking: knowledge of Ethernet and InfiniBand, switching, routing, and advanced data center networking automation 
• Linux: Proficiency in Linux system administration (user management, configuration, troubleshooting) 
• Storage: Understanding of file systems, storage protocols, and performance testing 
• Virtualization: Experience with VMs, containers, and GPU virtualization 
• AI Concepts: Familiarity with machine learning, deep learning, and common AI applications

Candidates must demonstrate comprehensive, up-to-date expertise in operating and managing AI data center environments, including compute, networking, storage, and virtualization. Ideal candidates have hands-on experience with advanced AI data center operations and workflows. 

Practical Experience: 

• Professional roles such as data center administrator, DevOps engineer, system administrator, AI infrastructure engineer, or data scientist working with enterprise-scale AI environments 
• Direct, practical experience with: 
• Operating and managing AI compute platforms (GPUs, CPUs, DPUs) 
• Provisioning and managing AI workloads and virtualization in data centers 
• Maintaining InfiniBand and Ethernet networks for AI workloads 
• Storage architecture and performance optimization for AI data centers 
• Virtualization technologies (VMs, containers, GPU partitioning) 
• Installing and managing NVIDIA software (GPU drivers, DOCA, NGC containers, AI Enterprise Suite) 
• Using management tools such as NVIDIA DCGM, UFM, and BlueField management utilities 

Knowledge and Expertise:

• Networking: knowledge of Ethernet and InfiniBand, switching, routing, and data center networking automation 
• Linux: Proficiency in Linux system administration (user management, configuration, troubleshooting) 
• Storage: Understanding of file systems, storage protocols, and performance testing 
• Virtualization: Experience with VMs, containers, and GPU virtualization 
• AI Concepts: Familiarity with machine learning, deep learning, and common AI applications

Candidates must demonstrate solid expertise in enterprise AI infrastructure, with hands-on experience deploying Kubernetes-based workloads on bare-metal servers and managing NVIDIA GPU environments at scale.

Practical Experience:

• IT professional with hands-on experience in enterprise data center environments (servers, storage, networking, GPUs, Linux)
• Experience deploying and managing Kubernetes clusters on bare-metal infrastructure
• Hands-on experience with containerization: Container and Kubernetes (CNCF)
• Experience with GPU-enabled workloads and NVIDIA GPU configuration in production
• Familiarity with NVIDIA AI Enterprise suite components (NIM, GPU Operator, DCGM Exporter)
• Experience deploying AI/ML workloads, preferably including RAG pipelines

Knowledge and Expertise:

• Data center infrastructure: servers, storage, networking, GPUs, and Linux operating systems
• Kubernetes cluster management and Helm chart deployments
• NVIDIA GPU Operator and GPU configuration on Kubernetes
• Monitoring and observability: DCGM Exporter, Prometheus, Grafana
• Linux system administration and bare-metal server management
• Basic understanding of AI/ML concepts and enterprise AI workflows

Preferred:

• NCA-AIIO certification