Solve complex system challenges: Design reliable workflows and integrations, making informed trade-offs around performance, scalability, data consistency, security, maintainability and cost.
Based on Southeast Asia business scenarios and Agent applications, participate in designing AI business implementation scenarios to help improve business efficiency and solve business pain points.
Proficiency in programming languages commonly used in AI (such as Python, C/C++, or Java) and relevant ML frameworks (for example, TensorFlow, PyTorch, or similar).
Design and evolve architectures that meet stringent requirements for reliability, resilience, scalability and cybersecurity, including safely running user-authored code and high volumes of LLM calls
Engineer Enterprise Knowledge Systems: Build and maintain knowledge platforms spanning codebases, architecture repositories, documentation, operational runbooks, and post-mortems to power context-aware AI agents and workflows.
Build the agentic-security surface. Partner on red-teaming and adversarial evaluation - tool-chain exploits, RBAC and permission-inheritance failures, multi-turn safety erosion - so we catch what a motivated adversary would.
Bachelor's degree or above in Computer Science or related fields, with 3+ years of engineering experience in at least one of Python, TypeScript, Java, or C/C++.
Domain expert: You've scaled AI products in production and design for LLM failure modes such as hallucination, context degradation, prompt injection and latency under load.
Conduct reinforcement learning (RL) training experiments for long-horizon tasks, including hyperparameter tuning, performance analysis, and iterative optimization.