AI Engineer (Full-time) — O₂ AI

AI Engineer (Full-time) — O₂ AI Just as Harvey is transforming the legal industry, O₂ AI is building a Vertical AI Agent for the global semiconductor and electronics supply chain. Company Website: We build deeply vertical AI Agents capable of handling high-stakes, high-complexity challenges in global manufacturing systems, transforming chaos into efficient, intelligent automated execution. We are looking for a hands-on Full-time Builder to own product engineering end-to-end: delivering core features, improving system reliability, and continuously expanding applications and the AI workflows behind them. Why Join Us?Elite Founding Team Work alongside founders from Yale, UC Berkeley, Stanford, and MIT, who bring both world-class academic foundations from top research labs and real-world "battle scars" from industry frontlines. Gain continuous access to firsthand insights and perspectives from Silicon Valley's startup and technology frontier. Real Customers, Real Impact At O₂ AI, we provide you with direct opportunities to communicate with customers. You will interface directly with US clients and collaborate with Fortune 500 electronics and semiconductor companies, building production-grade AI Agents that are truly deployed and operational. True 0 → 1 Ownership At O₂ AI, we create opportunities for you to deeply engage in product and engineering. You will participate in the complete process from problem definition and solution design to product launch and continuous iteration, gaining highly hands-on practical experience and witnessing the real-world impact of your work in customer business scenarios. Co-creating Cutting-edge AI with MIT O₂ AI collaborates deeply with MIT Media Lab to advance frontier AI research in real industrial scenarios, with opportunities to conduct joint research with the MIT team, publish papers, and translate research outcomes into production-grade vertical AI Agents. What You'll Work On (Core Responsibilities) End-to-end product and engineering development: From requirement understanding → architecture design → implementation → deployment → iteration, lead the complete product lifecycle Build and optimize AI Agent / LLM workflows, including: • Context Engineering, RAG (Retrieval-Augmented Generation) • Tool / Function calling, security and risk controls • Agent behavior evaluation, failure mode analysis and optimization Design and implement enterprise-grade SaaS capabilities: • Authentication, role/permission systems • Data model design and optimization • Multi-tenant architecture • System scalability and performance optimization Backend systems and data layer construction: • REST API design and implementation • SQL Schema design, index optimization • Database performance tuning • Caching strategies and implementation Frontend development and user experience: • Build modern UI with React + TypeScript • Implement complex interactions and state management • Optimize frontend performance and user experience Production environment operations and optimization: • Docker containerization deployment • Cloud service configuration and management (AWS/GCP/Azure) • CI/CD pipeline construction • System monitoring, log analysis, and troubleshooting Technical decisions and architecture design: • Participate in technology selection and architectural decisions • Code Review and technical standards development • Technical debt management and refactoring Customer engagement and requirement understanding: • Directly communicate requirements with US customers • Technical solution demos and explanations • Collect feedback and iterate rapidly What We're Looking ForRequired Skills Solid computer science foundation • Data structures and algorithms • System design and architecture • Networking and distributed systems basics Strong full-stack engineering capabilities: • Frontend: React + TypeScript, modern frontend engineering practices • Backend: Python / Node.js, able to build scalable services • At least 2-3 years of practical development experience (or equivalent capability) AI / LLM / Agent system understanding: • Deep understanding of LLM working principles and limitations • Prompt Engineering and Context design • RAG architecture design and implementation • Multi-step reasoning and Agent behavior design Database and data modeling: • SQL (PostgreSQL / MySQL) • NoSQL (MongoDB / Redis) • Schema design, query optimization DevOps and cloud services: • Docker / Kubernetes basics • AWS / GCP / Azure cloud services • CI/CD pipelines (GitHub Actions / GitLab CI) Excellent engineering discipline: • Code quality awareness, writing maintainable and testable code • Git workflow, Code Review capabilities • Documentation and technical communication skills Independent problem-solving ability: • Able to independently analyze problems, design solutions, implement features • Proactively seek best practices and optimization approaches • Quickly learn new technologies and tools Good English com