Agent Systems Engineer

Jobtailor; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

Jobtailor is seeking an experienced engineer to design and implement long-running, harness-based multi-agent architectures in our distributed systems stack. You will collaborate with research, product and engineering to translate agent outputs into actionable product insights.

The role emphasizes Python development, container orchestration with Docker and Kubernetes, and building robust tracing, cost monitoring and failure-detection mechanisms to ensure production reliability.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Python

Async programming

Distributed systems

Agent-based architectures

Cross-functional collaboration

Tools

Docker

Kubernetes

Job description

Responsibilities

• 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.

Requirements

• 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.

Core Competencies

Expertise in designing and implementing production-grade distributed systems and agent-based architectures, with a strong focus on Python programming, containerization, and orchestration. Proven ability to develop behavioral models and translate agent outputs into actionable insights for product decisions.