Senior SOCD Applied AI Engineer

at Nvidia

USD 168,000-264,500 per year

SENIOR

✅ Hybrid

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 4 API @ 7 CI/CD @ 6 Claude Code @ 4 Codex @ 4 Communication @ 6 LLM @ 4 LangChain @ 4 Observability @ 6 Python @ 7 RAG @ 4 Security @ 6 Vector Databases @ 4

Develop LLM-powered tools for high-value execution tasks: design review summarization, signoff status aggregation, integration checklist enforcement, CI/CD pipeline gating, and cross-team status reporting.
Build and deploy RAG-based knowledge systems grounded in internal design documentation and execution artifacts.
Design AI-assisted coding workflows, including agent-based development tools, reusable prompt templates, and structured skills to accelerate engineering productivity.
Own reliability and evaluation of AI systems, including logging, tracing, prompt regression testing, and output validation frameworks.
Collaborate closely with SOCD execution and methodology teams to scope problems, validate solutions, and define metrics for productivity gains from deployed automation.

BS/MS in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
6+ years of experience building production-grade software systems.
Proven experience shipping AI/LLM-powered applications, agents, or automation workflows into production environments.
Strong Python skills with the ability to design, prototype and productize AI-enabled services, APIs, integrations, automation workflows, and internal tools.
Practical experience building LLM-powered agents or agentic workflows, with hands-on use of Claude Code, OpenAI Codex, Cursor, or equivalent coding agents to improve development workflows.
Hands-on experience with LLM application frameworks (LangChain, LlamaIndex, or equivalent) and RAG architectures — including chunking, embedding models, vector databases, and retrieval design.
Solid software engineering fundamentals and production mindset, including system design, API design, testing, CI/CD, code quality, observability, security, databases, containers, and distributed or event-driven systems.
Ability to identify repetitive, high-friction, or knowledge-intensive workflows and turn them into practical AI-enabled tools, automations, or assistants that improve productivity and operational efficiency.
Demonstrated end-to-end ownership of engineering solutions, from architecture and development to deployment, integration, and ongoing operations/support.
Excellent communication skills and a collaborative, proactive approach.