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<p>As a Software Engineer specializing in AI agentic applications, you'll join a forward-thinking team dedicated to building both customer-facing and internal-facing intelligent chatbots and workflows that push the boundaries of agentic technology.</p> <p>You'll work on cutting-edge projects involving Retrieval-Augmented Generation (RAG), vector databases, and agentic frameworks, using Python and Go as your primary tools. This role offers the opportunity to design and deploy scalable, real-world solutions while collaborating with a dedicated team of Engineers. Your work will be visible in the Tesla mobile app and tesla.com, and will even be heard over the phone (when customers call our voice agents).</p> <ul> <li>Develop and implement AI agentic applications using Python and Go, leveraging modern agentic frameworks </li><li>Build and optimize Retrieval-Augmented Generation (RAG) systems for enhanced AI performance </li><li>Design and manage vector databases (e.g., pgvector, FAISS) for efficient data handling </li><li>Utilize agentic frameworks to create robust workflows for language models and agent coordination </li><li>Conduct evaluation of models to assess performance, accuracy, and reliability, iterating as needed </li><li>Perform prompt engineering and tuning to optimize large language model outputs for specific use cases </li><li>Write clean, maintainable, and well-documented code in Python and Go, adhering to industry standards </li><li>Collaborate with team members to define requirements and deliver production-ready solutions </li><li>Debug, troubleshoot, and enhance AI systems for scalability and reliability </li><li>Stay ahead of the curve by exploring advancements in AI and agentic technologies </li><li>Degree in Computer Science, Engineering, or a related field or equivalent experience </li><li>Strong proficiency in Python and Go, with proven experience in agentic frameworks </li><li>Hands-on experience with Retrieval-Augmented Generation (RAG) and vector database technologies </li><li>Knowledge of Agentic frameworks like LangChain and LangGraph for orchestrating AI workflows </li><li>Solid grasp of machine learning and natural language processing (NLP) principles </li><li>Strong understanding of data modeling and data architecture </li><li>Familiarity with data quality checks and validation processes </li><li>Exceptional problem-solving skills and meticulous attention to detail </li><li>Experience with streaming speech-to-text and text-to-speech technologies is a plus </li></ul>
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