AI Analytics and Visualization Engineer

NVIDIA

4d•Hybrid

About The Position

NVIDIA’s SCG - Silicon Co-Design Group, Efficiency (SSGE) team is seeking a full-stack developer passionate about building automation and resourceful workflow tools that streamline complex engineering and business processes. You will develop and maintain intelligent workflows that leverage LLMs and automation frameworks to orchestrate data collection, decision-making, and actions across systems, while also building robust data pipelines and interactive interfaces where needed. You will partner closely with other teams to understand their workflows and turn them into AI-powered agents and tools using modern frameworks and technologies. At NVIDIA, we strive for perfection, encourage innovation, and provide opportunities to explore new ways to succeed!

Requirements

MS or equivalent experience.
5+ years of full-stack development.
Strong Python skills with a track record of building APIs and backend services.
Proficient with SQL and relational or NoSQL databases, plus data modeling and workflow automation.
Familiar with LLM platforms and agent/tool frameworks, and able to turn sophisticated workflows into usable AI tools.
Expertise in Jira, Perforce, and Git-based platforms, with the technical ability to harness APIs for data retrieval and integration.
Excellent problem-solving skills, attention to detail, and a collaborative approach.

Nice To Haves

Skilled in deploying AI services on AWS, Azure, or GCP with Docker or Kubernetes.
Experience with event-driven or streaming systems (e.g., Kafka, Kinesis).
Expertise in dashboarding tools (e.g., PowerBI, Grafana, or EasyBI).
Hands-on use of AI development tools (e.g., Cursor, Copilot).
Experience leveraging AI tools or large language models (LLMs) to enhance productivity, automate workflows, or improve data-driven decision-making.

Responsibilities

Build and run AI agent workflows involving LLMs and tools to automate multi-step processes.
Build orchestration services that coordinate model calls, tools, and human review.
Develop APIs that expose AI agents to internal apps and UIs or equivalent experience.
Connect existing ETL and data pipelines as tools and context for agents.
Construct simple web or chat interfaces for workflows powered by artificial intelligence.
Ensure scalability, performance, and security of AI services and data flows.