Senior AI Agent Architect & Tech Team Lead

Pearson Carter

Queensland

On-site

AUD 120,000 - 180,000

Full time

11 days ago

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Job summary

Pearson Carter is seeking a senior technical leader to design, build and maintain production agentic systems end to end. You will guide a small technical team, set standards for architecture and tooling, and own compute decisions including cost and latency.

The role combines hands-on delivery with strategic input on AI strategy and tooling choices. You will work closely with the Founder to shape platform direction, evaluate integrations, and ensure security and privacy are embedded from day one.

Qualifications

Demonstrated experience delivering agentic systems in production: OpenClaw, Claude Agent SDK and MCP, LangGraph, CrewAI, Microsoft Agent Framework, n8n or comparable platforms.
Strong API and integration capability: REST, OAuth, webhooks, rate limiting, retry logic and error handling.
Sound judgement on compute and hardware: local versus hosted deployment, resource sizing, inference cost, latency and scaling.
A security first approach: credential and key management, permissions, sandboxing, audit logging, and handling of personal information consistent with privacy laws.
Awareness of adversarial risk in agentic systems: prompt injection, untrusted input handling and tool use containment.
Operational discipline: version control, evaluation and testing, monitoring, model and dependency updates, and documentation.
Leadership capability: guide a small technical team, review work, raise the standard.
Clear communication: document a design for non-technical colleagues before building it.
Seniority and judgement: the confidence to disagree with the Founder, and the evidence to back it.

Responsibilities

Design, build, deploy and maintain production agentic systems end to end.
Guide and develop a small technical team, and set the standard for how work is designed and built.
Own architecture and compute decisions, including local versus hosted, resource sizing, inference cost, latency and scaling.
Build and maintain integrations across the group’s systems, and keep them running.
Establish evaluation and testing practice so agent quality is measured, not assumed.
Implement monitoring, logging and alerting appropriate to LLM based systems.
Manage model, dependency and platform updates without breaking production.
Design for security and privacy from the outset, including handling of student personal information.
Govern cost, including token budgets, per agent unit economics and cost visibility.
Document designs and explain them clearly to non-technical colleagues before building.
Advise the Founder on AI strategy, tooling, build versus buy, and technical risk.