AI Staff Engineer

We're currently looking for an AI/ML Staff Engineer to join our financial services client in Sydney on a 6 month, rolling contract to lead the design of complex software systems

The role will encompass both hands-on engineering & leadership; you will lead technical delivery from initial system design through to development and production, while mentoring more junior devs and enhancing the team’s overall capability and productivity.

Key Responsibilities

• Architectural Strategy & Vision: Lead the design of complex software systems, advocating for a "target state" architecture that prioritizes resilience, security, scalability, and cost-effectiveness across the domain.
• Technical Leadership & Execution: Drive the end-to-end development of innovative solutions to address complex technical challenges, ensuring all implementations align with business needs and engineering best practices.
• Mentorship & Collaborative Culture: Provide high-level technical guidance and mentorship to engineers, fostering a culture of knowledge sharing and collaboration within your squad and across upstream/downstream partners.
• Cross-Functional Partnership: Collaborate with product managers, designers, and architects to translate business requirements into exceptional digital experiences, bridging the gap between technical constraints and product goals.
• Engineering Excellence & Innovation: Champion the evolution of engineering practices and the adoption of emerging technologies to continuously improve team efficiency, system quality, and operational scalability.

Required Skill Set

• Agentic Architecture & Control Patterns: Proven experience building agent loops (ReAct, Planning/Decomposition, Reflection, Multi-Agent Orchestration) and handling non-deterministic system design.
• Tooling & API Design: Experience defining strict JSON/OpenAPI schema interfaces optimized for LLM function calling and self-correcting error handling.
• State & Context Engineering: Expertise in state machine design, long-term memory (Vector/Graph RAG), and dynamic context window optimization (summarization, token budget management).
• Eval-Driven Development (EDD): Hands-on experience building automated evaluation pipelines (combining unit tests, schema checks, and LLM-as-a-Judge) to benchmark agent accuracy and prevent regressions.
• Agent Observability & Tracing: Proficiency with deep trajectory tracing tools (e.g., LangSmith, Phoenix, OpenTelemetry) to monitor execution graphs, latency, and token costs.
• Human-in-the-Loop (HITL) & Safety: Track record of designing HITL approval controls, sandboxed code execution (Docker/E2B), and guardrails against prompt injections or unintended tool actions.
• Technical Leadership & Pragmatism: Ability to define SLAs for non-deterministic products and know when to use deterministic code over fragile open-ended agent loops.

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