JR-193246 AI Application Engineer

Role Overview

Designed and developed production-grade LLM-powered enterprise applications using Python, LangChain, and Retrieval-Augmented Generation (RAG) architectures. Built end-to-end AI pipelines incorporating prompt engineering, query rewriting, intent classification, embeddings, vector databases, retrieval, reranking, and response synthesis to deliver accurate, low-latency responses.

Responsibilities

• Design and implement end-to-end LLM application pipelines using modern orchestration frameworks.
• Own prompt engineering, model quality, RAG accuracy and AI safety across multiple AI applications.
• Build and optimize multi-step LLM workflows including intent classification, query rewriting, retrieval, reranking and response synthesis.
• Develop scalable Retrieval-Augmented Generation (RAG) systems with high retrieval precision and low latency.
• Implement production-grade AI guardrails, jailbreak protection, prompt injection mitigation and hallucination reduction strategies.
• Design automated evaluation pipelines for prompts, retrieval quality and model performance.
• Optimize latency, throughput and cost across complex LLM call chains.
• Integrate NVIDIA AI technologies including NIM, NeMo, NeMo Guardrails and NVIDIA Riva.
• Build streaming AI APIs and conversational experiences supporting multi-turn interactions.
• Collaborate with platform, backend and product engineering teams to deliver enterprise AI solutions.
• Monitor production AI systems, identify quality degradation and continuously improve model performance.

Requirements

• 4+ years of software engineering experience.
• At least 2 years building production LLM-powered applications.
• Expert-level Python development.
• Strong experience with LLM prompt engineering and prompt optimization.
• Experience designing production RAG architectures.
• Experience with LangChain, LlamaIndex or custom orchestration frameworks.
• Strong understanding of embeddings, vector databases and retrieval optimization.
• Experience implementing AI safety, guardrails and prompt injection protection.
• Experience designing automated AI evaluation pipelines (RAGAS, TruLens or equivalent).
• Experience optimizing latency and quality across multi-stage LLM pipelines.
• Experience developing streaming AI APIs.
• Strong understanding of REST APIs and asynchronous Python development.
• Experience deploying AI applications using NVIDIA NIM and NeMo technologies.
• Excellent debugging, analytical and communication skills.
• Strong English communication skills.

Skills

• Python
• LangChain
• RAG
• NVIDIA NeMo
• Vector Databases