Tech Lead-Machine Learning Engineer (Agent & Multi-Agent Systems) – AIGC Risk Intelligence

What you'll do

• Design agent workflows, tool-augmented reasoning, memory architectures, and multi-agent orchestration.
• Build risk detection and feedback-driven optimization systems with execution traceability and observability.
• Own the technical roadmap, establish engineering standards, and mentor engineers while partnering across teams.

What they ask for

• 5+ years of experience in software engineering or applied AI systems.
• Deep understanding of LLM-based agent architectures, including tool calling, workflow orchestration, and memory design.
• Experience designing distributed or modular AI systems and operating in adversarial or high-stakes environments.
• Strong backend engineering skills in Python or equivalent.

Nice to have

• Experience in trust & safety, risk detection, adversarial ML, or multi-agent orchestration frameworks.
• Experience with execution trace logging, observability, RL-style policy optimization, or leading high-impact technical initiatives.

Tools

Python, LLM, ReAct

AI focus

Builds tool-augmented, multi-agent AI systems for proactive AIGC risk detection, with trace-driven feedback and iterative system improvement.