NVIDIA AI Engineer / Agentic AI Systems Engineer
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- Build accelerated NVIDIA AI applications and production-ready agents on NIM and Nemotron
- Ground agents in enterprise data and connect interoperable, governed multi-agent systems
- Optimize inference, evaluate quality and operate safe agent systems at GPU scale
Benchmark: NVIDIA Senior AI Systems Engineer + Agentic AI Engagement / Applied AI engineering signals (current Sep 2026). Market refresh: NVIDIA NIM and Nemotron; NeMo Agent Toolkit; RAG and enterprise retrieval; MCP/A2A-style interoperability; evals and observability; NeMo Guardrails; TensorRT-LLM and Dynamo; GPU-aware inference optimization and production reliability.
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Enrichment
Build accelerated NVIDIA AI applications and production-ready agents on NIM and Nemotron
NIM, Nemotron + accelerated application engineering
Coverage: NVIDIA NIM / Nemotron / Python / APIs / Containers / GPU-Aware Model Serving
NeMo Agent Toolkit + workflow engineering
Coverage: NeMo Agent Toolkit / Tools / FastAPI / Orchestration / Multi-Agent / Profiling
Ground agents in enterprise data and connect interoperable, governed multi-agent systems
Enterprise RAG + multimodal retrieval
Coverage: NeMo Retriever / Embeddings / Reranking / Vector Search / Multimodal RAG / Enterprise Data
MCP, tool interoperability + secure agent integration
Coverage: MCP / NIM / NeMo Agent Toolkit / Enterprise APIs / Multi-Agent / Security Boundaries
Optimize inference, evaluate quality and operate safe agent systems at GPU scale
TensorRT-LLM, Dynamo + inference performance
Coverage: TensorRT-LLM / NVIDIA Dynamo / GPU Serving / Throughput / Latency / Distributed Inference
Agent evaluation, guardrails + production reliability
Coverage: NeMo Agent Toolkit Evals / Observability / NeMo Guardrails / Safety / Cost / Failure Analysis
Role-Readiness Proof / Capstone
Build and operate a production NVIDIA agentic system for a real enterprise workflow. Serve a Nemotron or approved open model through NIM; implement the workflow with NeMo Agent Toolkit; ground it with enterprise retrieval and reranking; integrate at least two tools and an MCP path; add a multi-agent branch only where it improves task quality; deploy GPU inference with TensorRT-LLM or Dynamo and profile throughput, latency and cost; add NeMo Guardrails, identity/secrets controls, agent evals, traces and failure analysis; load-test the system, run a security/failure drill and deliver a reproducible containerized deployment, runbook and business-task readout.
Proof Rubric
- Prove correct NIM and model choice
- reliable Python and agent implementation
- grounded retrieval
- clean tool and MCP contracts
- useful NeMo Agent Toolkit orchestration
- evaluation and profiling evidence
- safe guardrail and permission boundaries
- TensorRT-LLM or Dynamo inference judgment
- GPU utilization, latency, throughput and cost awareness
- production observability and recovery
- measurable task success
- and clear tradeoffs among model size, inference strategy, retrieval, single-vs-multi-agent, open-vs-managed components and quality-vs-performance