Back
Tier-1 employer, mid-level generalist title, and metro location drive high applicant competition.
Specialized conversational AI, LLMs, GPU inference, and embedded targets limit cross-industry transferability.
Explicit 2+ years, mandatory Python, LLM/Conversational AI, CI/CD and container skills increase filtering stringency.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own quality assurance for NVIDIA's voice AI pipelines integrating ASR, LLM, and TTS models across cloud, workstation, and edge targets.
Develop and maintain Python-based test frameworks and AI-powered test automation for multi-model conversational AI systems, focusing on latency, accuracy, and interruption scenarios.
Manage and improve CI/CD pipelines and code coverage; create test plans and benchmarks for multi-agent orchestration and multimodal AI features.
Bachelor's or Master's degree in CS, CE, IT, ECE, or EEE.
2+ years of hands-on testing experience in software or embedded systems.
Strong Python programming skills with ability to develop test frameworks from scratch; proficiency with Linux, shell scripting, and command-line debugging.
Experience with Conversational AI systems (ASR, TTS, NLP pipelines), LLMs, CI/CD pipelines (GitHub Actions, Jenkins), Docker, and QA processes including code coverage and regression management.
Experienced in testing and automating real-time streaming or voice/audio pipelines.
Familiarity with NVIDIA NIM microservices, GPU-accelerated inference, and Kubernetes or edge deployment platforms (e.g., Jetson, DGX Spark).
Demonstrated ability to develop AI Agents and agentic workflows using frameworks such as LangChain or Pipecat for QA automation.