Design and implement long-running, harness-based multi-agent architectures — covering topologies, orchestration, memory management, tool registries, and execution environments.
Develop behavioral and persona models with goal-directed simulations, including evaluation frameworks, prompt schemas, and automated or human-in-the-loop feedback mechanisms to measure fidelity and surface failure modes.
Build signal and insight systems that translate agent outputs into actionable product decisions, enabling data-driven iteration across research, product, and engineering teams.
Ensure production reliability through comprehensive tracing, cost monitoring, failure detection, and robust fallback strategies.
Collaborate cross-functionally with distributed systems, machine intelligence, and product teams to deliver measurable, scalable agent capabilities.

5+ years of hands-on experience designing and implementing production-grade distributed systems and agent-based architectures
Strong proficiency in Python, with substantial async programming experience.
Demonstrated experience building production ML systems, including tracing, cost monitoring, failure detection, and robust fallbacks.
Hands-on experience with containerisation and orchestration (Docker, Kubernetes) for large-scale agent infrastructures.
Experience designing behavioral models, goal-directed simulations, evaluation frameworks, prompt schemas, and human-in-the-loop feedback mechanisms.
Proven ability to translate agent outputs into actionable product decisions; experience building signal and insight systems.
Strong cross-functional communication and collaboration skills across technology, research, and product.

Agent Systems Engineer

Key skills