Role Overview: As a Full Stack Software Engineer, you will be a pivotal force in developing, deploying, and maintaining the end-to-end infrastructure for our advanced AI systems. This includes designing robust backend services, building intuitive and high-performance user interfaces, and ensuring the seamless integration of LLM-based AI Agents. Your expertise will bridge the gap between frontend user experience, backend scalability, and core AI infrastructure, directly impacting system efficiency, reliability, and user-facing capabilities. What You'll Do: - Architect, develop, and maintain scalable full-stack components, including both frontend applications (using modern frameworks like React/Vue/Angular) and robust backend services (leveraging Python/Go/Node.js). - Design and implement APIs and data pipelines that facilitate the smooth deployment and interaction of sophisticated AI Agents and large-scale data processing workflows. - Contribute to the development of core AI agent frameworks, focusing on features like tool integration, memory systems, and planning/orchestration modules. - Develop and implement AI Agent evaluation methodologies and tooling to rigorously test, benchmark, and monitor agent performance, reliability, and safety in production. - Manage and optimize cloud infrastructure (e.g., AWS, GCP, Azure) to ensure high availability, cost-efficiency, and scalability for both the application layer and the underlying AI compute resources. - Participate actively in design discussions, code reviews, and cross-team collaboration to deliver high-quality, production-grade solutions across the entire stack. Minimum Qualifications: - Bachelor's degree in Computer Science, Computer Engineering, related technical discipline, or equivalent practical experience. - 3+ years of experience building and maintaining full-stack software infrastructure, with proven expertise in both frontend and backend development. - Hands-on experience building AI agents, AI agent frameworks/orchestration systems, or complex LLM-powered applications and workflows (e.g., RAG pipelines, multi-agent systems, prompt chaining architectures, or LLM orchestration frameworks). - Practical knowledge of cloud infrastructure management (e.g., Docker, Kubernetes, Terraform) and CI/CD pipelines. - Proven expertise in designing, scaling, and optimizing enterprise-grade ML or data-intensive systems. Preferred Qualifications: - Master's or Ph.D. degree in Computer Science, Computer Engineering, or a related technical discipline. - Demonstrated experience developing and managing large-scale distributed systems and high-throughput AI infrastructures. - Expertise in a modern frontend framework (e.g., React, Vue, Angular) and associated state management libraries. - Experience in developing and deploying AI Agent Evaluation frameworks (e.g., using tools like LangSmith, Arize, or custom evaluation metrics). - Demonstrated success building production LLM applications with complex workflows such as autonomous agents, conversational AI systems, or intelligent automation platforms.