Description

Senior AI / ML / Agent Engineer to join a Azure Quantum programme — a hands-on builder role at the intersection of agentic AI, applied machine learning, and quantum-inspired computing.

You'll work alongside quantum specialists, data engineers, and domain experts to take high-value use cases from problem framing through to working solution.

This is a build role, not an advisory one. You'll be writing code daily — building agents, training and tuning models, integrating with Azure Quantum and quantum-inspired optimisation services, and standing up the AI/ML scaffolding that surrounds the quantum workloads. Expect ambiguity, fast iteration, and the need to make pragmatic calls about when to use a classical model, when to reach for an agentic pattern, and when quantum or QIO is the right tool.

Key responsibilitiesDesign and build agentic systems on Azure AI Foundry / Semantic Kernel — including orchestration, tool use, memory, and evaluation

Develop, train, and deploy machine learning models supporting quantum and quantum-inspired use cases (optimisation, simulation, materials, logistics)

Integrate AI/ML components with Azure Quantum workloads, quantum-inspired optimisation (QIO) services, and classical Azure compute

Build data pipelines and feature engineering workflows in partnership with the Fabric data engineer

Implement evaluation frameworks for both ML models and agent behaviours — including safety, accuracy, and cost metrics

Productionise experimental work — move from notebook to deployed service with appropriate testing, monitoring, and CI/CD





Partner with BHP domain experts to translate operational problems into AI/ML and quantum-suitable formulations

Contribute to architectural decisions across the AI/ML/quantum stack

Requirements

Experience

7+ years building and deploying ML systems in production environments

Demonstrated hands-on experience building agentic systems — Azure AI Foundry, Semantic Kernel, LangChain, AutoGen, or equivalent

Production experience with LLMs — RAG, fine-tuning, prompt engineering, evaluation, and guardrails

Track record of taking ML work from prototype to production, including MLOps practices

Experience in resources, mining, or heavy industry highly regarded but not required

Technical skills — must-have

Strong Python — daily working language, including ML libraries (PyTorch or TensorFlow, scikit-learn, NumPy, pandas)

Hands-on experience with Azure AI services — Azure AI Foundry, Azure OpenAI, Azure ML, or equivalent

Demonstrated ability to build, evaluate, and deploy AI agents — not just call an LLM API

Solid software engineering fundamentals — Git, testing, CI/CD, containerisation (Docker), API design

Comfortable with SQL and modern data tooling (Fabric, Synapse, Databricks, or similar)





Technical skills — highly regarded

Azure Quantum, Q#, or quantum-inspired optimisation (QIO) experience

Optimisation and operations research background — linear programming, combinatorial optimisation, constraint solvers

Materials science, computational chemistry, or simulation experience

Reinforcement learning, particularly for control or optimisation problems

Experience with vector databases and retrieval systems (Azure AI Search, pgvector, Pinecone, etc.)

Engineering practice

Writes clean, maintainable, tested code — not just notebooks

Comfortable working across the stack — data, models, agents, deployment, monitoring

Strong evaluation discipline — measures before claiming, knows the difference between a demo and a system that works

Bias to shipping — moves from problem to working code quickly without skipping the basics

Qualifications

Tertiary qualification in Computer Science, Engineering, Mathematics, Physics, or related STEM discipline

Postgraduate qualifications in ML, AI, optimisation, or quantum computing — strong advantage

Microsoft certifications (AI-102, AZ-900 minimum) — advantage

Attributes

Comfortable with ambiguity and rapid learning curves

Pragmatic about tooling — uses the right approach for the problem, not the one that's trendy

Strong written communication — can explain technical work to both engineers and business stakeholders

Collaborative — works well with quantum specialists, data engineers, and domain experts

📌 Senior AI/ML/Agent Engineer (Perth)
🏢 SG Consulting
📍 Perth