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

Our team is building the next generation of AI-native risk intelligence systems to address emerging challenges driven by large-scale AIGC content production.

As content creation becomes automated and adversaries adopt systematic experimentation (e.g., large-scale template variation and rapid iteration), traditional rule-based and single-model approaches are no longer sufficient. We are transitioning from monolithic LLM applications to a structured multi-agent architecture that emphasizes:

Tool-augmented reasoning (ReAct-style systems)

Modular skill composition

Execution traceability and observability

Feedback-driven system evolution

Cross-domain risk reasoning

We are seeking an experienced technical leader to define and implement this architecture.

Responsibilities

• Architect and Implement Agent Systems

• Design structured agent workflows (e.g., Evaluate → Validate → Reflect → Summarize)
• Implement ReAct-style tool allocation and reasoning frameworks
• Develop short-term and long-term memory architectures
• Ensure robustness under adversarial and evolving conditions

• Lead Multi-Agent Architecture Development

• Design orchestration layers for coordinating vertical domain agents
• Build modular Skill systems for extensibility and reuse
• Define execution graph standards and planning abstractions
• Establish traceability mechanisms for debugging and auditability

• Develop Open Risk Detection Capabilities

• Architect systems capable of identifying previously unseen risk patterns
• Implement execution trace–driven optimization loops
• Translate feedback signals (FP/FN, reviewer overrides, drift signals) into system improvements
• Enable proactive rather than purely reactive detection systems

• Establish Engineering Standards for Agent Systems

• Define traceability, observability, and guardrail requirements
• Evaluate and integrate multi-agent frameworks where appropriate
• Ensure production-readiness, scalability, and reliability

• Provide Technical Leadership

• Own the technical roadmap for Agent and multi-agent systems
• Partner cross-functionally with Risk, Safety, Infra, and ML teams
• Mentor engineers and drive architectural rigor

Minimum Qualifications

• 5+ years of experience in software engineering or applied AI systems
• Deep understanding of LLM-based agent architectures (ReAct-style reasoning/Tool calling systems/Workflow orchestration/Memory design patterns)
• Experience designing distributed or modular AI systems
• Strong backend engineering skills (Python or equivalent)
• Experience operating systems in adversarial or high-stakes environments

Preferred Qualifications

• Experience in trust & safety, risk detection, or adversarial ML
• Familiarity with multi-agent orchestration frameworks
• Experience building systems with execution trace logging and observability
• Background in RL-style policy optimization or iterative system refinement
• Demonstrated experience leading high-impact technical initiatives