The Engineering and Technology team is at the core of the Shopee platform development. The team is made up of a group of passionate engineers from all over the world, striving to build the best systems with the most suitable technologies. Our engineers do not merely solve problems at hand; We build foundations for a long-lasting future. We don't limit ourselves on what we can or can't do; we take matters into our own hands even if it means drilling down to the bottom layer of the computing platform. Shopee's hyper-growing business scale has transformed most "innocent" problems into huge technical challenges, and there is no better place to experience it first-hand if you love technologies as much as we do.

About Team

The AI Solutions team builds and pioneers the future of AI-driven automation at Sea. We are the developers of Smart (Sea Multi-Agent Realization Platform) - the company's flagship AI agents platform that enables teams across the company to automate their workflows using large language model (LLM) agents. But Smart is just the beginning.

Our team is also spearheading a wide range of AI initiatives, especially within the engineering infrastructure domain, from internal developer tooling to intelligent operations. We believe that the future of infrastructure lies in intelligent automation, and we’re committed to building it from the ground up.

We thrive in a fast-paced, sharing-first culture, with a strong emphasis on learning, creativity, and experimentation. We work hard and play hard, explore ideas on the cutting edge, and aim to pioneer bold, practical solutions to complex real-world problems. If you want to shape how AI changes engineering at scale - this is the place.

• Design and develop the Smart platform and other AI systems, covering all essential agent platform components - agent runtimes, toolchains, memory, orchestration, logging, planning, and sandboxing.
• Build scalable backend infrastructure to support agent-based workflows across the company.
• Collaborate with teams across departments - both tech and non-tech - to land and scale real-world AI use cases.
• Drive internal agent adoption in engineering infra by replacing traditional operations with intelligent agent workflows.
• Prototype and optimize agent architectures, including Reasoning and Action (ReAct) patterns, multi-step planning, and decision workflows.
• Work on platform reliability and observability to ensure our systems are performant, debuggable, and production-ready.
• Support operations and development work in a healthy balance - you’ll gain experience in shipping features and running real services.
• Build full-stack systems (primarily backend, with occasional frontend) that integrate well into internal ecosystems.
• Manage and operate supporting infrastructure, including vector stores, retrieval systems (RAG), and middleware components.
• Continuously experiment, learn, and bring in the latest advancements from the AI/agent ecosystem into production.

• Bachelor’s degree or higher in Computer Science, Engineering, or a related field.
• 2+ years of relevant experience in software development - or fresh graduates with strong fundamentals and hunger to learn.
• Strong programming skills in Python (primary) - familiarity with Golang is a plus.
• Solid CS fundamentals - algorithms, data structures, networking, systems, and architecture.
• Backend experience designing cloud-ready services using databases, queues, caching, etc.
• Familiarity with modern AI agent frameworks like LangChain, LangGraph, or strong interest in learning them.
• Strong problem-solving mindset and ability to navigate ambiguity with curiosity and creativity.
• Willingness to work full stack - we prioritize backend but value well-rounded engineers.
• General understanding of distributed systems and cloud-native principles (e.g. the twelve-factor app), including how services are deployed, scaled, and load-balanced in a containerized environment.
• Passion for building AI systems that people actually use.

Skills below are optional but preferred:

• Experience building AI-powered platforms, assistants, or automation tools.
• Familiarity with agent patterns like ReAct (Reasoning and Action), tool chaining, or multi-agent orchestration.
• Knowledge of RAG systems, vector databases, or prompt tuning.
• Prior experience in developer tools, internal platforms, or large-scale systems.
• Exposure to observability, debugging, incident workflows, or service reliability.