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software engineer

ai engineer - agentic systems

sydney, new south wales
posted 15 june 2026
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summary

  • sydney, new south wales
  • au$ 140,000 - au$ 170,000 per year
  • permanent

posted 15 june 2026

reference number
90M0709860_5294841153575560632

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job details

About the Role

As an AI Engineer, you will serve as a technical authority embedded within high-performing product and domain squads. You will transcend the boundary between data science and software engineering to design, build, evaluate, and scale robust, secure, and clear AI-enabled systems. Your daily mission will focus on taking advanced AI capabilities (including LLMs and complex agentic workflows) out of the lab and into repeatable, enterprise-grade production.

What's in it for you

  • Enterprise Impact: Shape the technical culture, architecture, and evaluation frameworks of a tier-one financial institution's AI capability.

  • Modern Stack: Work with advanced Generative AI, agent reasoning loops, RAG pipelines, and automated evaluation.

  • Cooperative Culture: Partner closely with talented AI specialists, domain experts, and product managers in an environment that prioritises innovation and customer focus.

Key Responsibilites

  • Agentic System Development: Implement agent reasoning loops, tool use interfaces, memory systems, context management, and orchestration architecture.

  • Production-Grade Delivery: Translate sophisticated architectural designs into robust, tested, cloud-native code.

  • Prompt & Context Engineering: Design advanced prompting strategies, retrieval pipelines (RAG), and context assembly to ensure highly reliable agent behaviour.

  • Responsible AI Alignment: Ensure all AI behaviours are clear, auditable, and strictly aligned with enterprise ethical and safety standards.

  • Rapid Experimentation: Test hypotheses regarding agent design, critically reviewing empirical data to inform long-term architectural pathways.

At Randstad, we are passionate about providing equal employment opportunities and embracing diversity to the benefit of all. We actively encourage applications from any background.

...

About the Role

As an AI Engineer, you will serve as a technical authority embedded within high-performing product and domain squads. You will transcend the boundary between data science and software engineering to design, build, evaluate, and scale robust, secure, and clear AI-enabled systems. Your daily mission will focus on taking advanced AI capabilities (including LLMs and complex agentic workflows) out of the lab and into repeatable, enterprise-grade production.

What's in it for you

  • Enterprise Impact: Shape the technical culture, architecture, and evaluation frameworks of a tier-one financial institution's AI capability.

  • Modern Stack: Work with advanced Generative AI, agent reasoning loops, RAG pipelines, and automated evaluation.

  • Cooperative Culture: Partner closely with talented AI specialists, domain experts, and product managers in an environment that prioritises innovation and customer focus.

Key Responsibilites

  • Agentic System Development: Implement agent reasoning loops, tool use interfaces, memory systems, context management, and orchestration architecture.

  • Production-Grade Delivery: Translate sophisticated architectural designs into robust, tested, cloud-native code.

  • Prompt & Context Engineering: Design advanced prompting strategies, retrieval pipelines (RAG), and context assembly to ensure highly reliable agent behaviour.

  • Responsible AI Alignment: Ensure all AI behaviours are clear, auditable, and strictly aligned with enterprise ethical and safety standards.

  • Rapid Experimentation: Test hypotheses regarding agent design, critically reviewing empirical data to inform long-term architectural pathways.

At Randstad, we are passionate about providing equal employment opportunities and embracing diversity to the benefit of all. We actively encourage applications from any background.

  • key responsibilities

    • Design and implement agent reasoning loops, memory systems, and orchestration logic
    • Translate architectural designs into robust, production-grade AI implementations
    • Design and embed rigorous evaluation strategies and observability pipelines for probabilistic systems
    • Partner with cross-functional product and engineering teams to deliver measurable AI business outcomes
    • Conduct rapid experimentation to test hypotheses and build empirical evidence for agent design.
  • qualification

    • Experience delivering agentic agents
    • Direct hands-on experience with prompt and context engineering
    • RAG
    • tool use patterns and agent orchestration

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