AI Application Engineer

JOB SUMMARY This role is for an AI Application Engineer to support the development and delivery of next-generation AI-powered applications. The position will concentrate on production-grade LLM application engineering, RAG quality, prompt engineering, AI safety, and the orchestration of complex multi-step AI pipelines. The engineer will design, develop, and optimize AI applications, ensuring AI quality, RAG accuracy, prompt engineering, and AI safety. Key activities include developing and maintaining orchestration pipelines, implementing and optimizing RAG pipelines, designing conversational AI experiences, integrating NVIDIA technologies, building automated evaluation pipelines, performing latency profiling, and implementing AI safety guardrails. Collaboration with global teams and support for production deployments are also integral to the role. Key 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. Required Qualifications 47 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. Education: Bachelors Degree