AI Application Engineer

Role: AI Application Engineer
Location: Santa Clara, CA (3 days onsite in a week)


Overview:
AI Application Engineer to support the development and delivery of next-generation AI-powered applications built on NVIDIA infrastructure. This role will focus on production-grade LLM application engineering, RAG quality, prompt engineering, AI safety, and orchestration of complex multi-step AI pipelines.

Day-to-Day Responsibilities
Design, develop, and optimize production-grade LLM-powered applications
Own AI quality, RAG accuracy, prompt engineering, and AI safety across multiple applications
Develop and maintain multi-step LLM orchestration pipelines using LangChain, LlamaIndex, or custom frameworks
Implement and optimize RAG pipelines including chunking strategies, embedding selection, reranking, and hybrid search
Design multi-turn conversational AI experiences with context management and session memory
Integrate NVIDIA technologies including NIM, NeMo, NeMoGuardrails, and Riva into enterprise AI applications
Build automated evaluation pipelines for model quality, hallucination detection, regression testing, and release gating
Perform latency profiling and optimization across multi-step LLM call chains
Implement AI safety guardrails including prompt injection prevention, jailbreak mitigation, and topical control
Collaborate with globally distributed engineering and product teams to deliver scalable AI solutions
Support deployment, monitoring, and continuous improvement of AI applications in production environments

Basic Qualifications:
4–7 years of software engineering experience with at least 2 years focused on production LLM application development
Expert-level experience with Python for AI/ML application development and async programming
Strong expertise in prompt engineering including system prompts, few-shot prompting, and instruction tuning
3+ Years of Hands-on experience with multi-step LLM orchestration frameworks such as LangChain or LlamaIndex
3+ Years of Experience designing and optimizing RAG pipelines and retrieval systems
3+ Years of Experience with vector databases, similarity search tuning, and reranking techniques
3+ Years of Hands-on experience with NVIDIA NIM, NeMo, NeMoGuardrails, and Riva
3+ Years of Experience implementing AI safety and guardrails for customer-facing applications
Strong knowledge of automated AI evaluation frameworks such as RAGAS or TruLens
3+ Years of Experience profiling and optimizing latency in multi-step AI pipelines
Ability to work onsite in Santa Clara, CA
Preferred Qualifications
Experience with adaptive learning systems or recommendation engines
Knowledge graph integration experience with RAG architectures
Experience with multi-agent orchestration patterns
ServiceNow API integration experience
Prior experience building AI products on NVIDIA infrastructure
Experience with streaming LLM response handling and real-time AI applications

Technology Stack
Python
LangChain
LlamaIndex
NVIDIA NIM
NeMo
NeMoGuardrails
NVIDIA Riva
Vector Databases
RAGAS / TruLens
LLM APIs and orchestration frameworks
Education
Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence, or equivalent work experience.