AI Engineer

Fortescue; Perth; On-site; AUD 110,000 - 170,000; Full time

Job summary

Fortescue seeks an AI Engineer to design, build and operate secure, scalable AI solutions across ML, generative AI, and RAG. You will translate business problems into production-ready services and support end-to-end MLOps and LLMOps.

You will collaborate with data scientists, engineers, architects, and cyber security teams to balance speed, reliability, governance and cost while delivering measurable business value.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence or a related field, or equivalent practical experience.
• Minimum 2+ years’ experience designing, developing and delivering AI/ML or cloud-native software systems, including solutions from prototype through to production.
• Proficiency in Python, including clean code, testing, API development, debugging and software design fundamentals.
• Practical understanding of machine learning and LLM concepts, model APIs, prompting, structured outputs, embeddings, vector search and RAG.
• Practical experience or strong applied understanding of agentic systems, including tool/function calling, workflow orchestration, context or state management, guardrails and human-in-the-loop controls.
• Experience evaluating AI quality and reliability, monitoring production behaviour, tracing model and tool activity, and managing latency, configuration and cost trade-offs.
• Working knowledge of AWS or Azure, containers, Kubernetes, infrastructure as code, CI/CD, automated testing and observability practices.
• Experience with SQL, data pipelines, object storage, REST APIs, event-driven or microservices architectures and governed enterprise data integration.
• Understanding of authentication, authorisation, secrets management, data privacy, secure tool use, AI risk controls and responsible deployment practices.
• Strong analytical, systems-thinking, problem-solving and communication skills.

Responsibilities

• Develop and maintain reusable AI components, APIs, microservices, model-serving capabilities and orchestration patterns.
• Implement machine learning, generative AI and RAG solutions using architectures appropriate to the business need.
• Build capabilities for automated testing, deployment, evaluation, monitoring, cost management and lifecycle management across MLOps and LLMOps.
• Design and implement bounded agentic workflows using approved enterprise data, tools and APIs.
• Apply appropriate patterns for tool calling, context and state management, orchestration, human approval, guardrails and safe fallback behaviour.
• Evaluate and monitor AI and agent workflows for task success, reliability, safety, latency, cost and operational value.
• Integrate AI services and agents into enterprise business workflows in alignment with architecture, cyber security, data governance and responsible AI requirements.
• Build secure integrations using appropriate identity, authorisation, secrets management, least-privilege access and auditability controls.
• Prototype and experiment to validate technical feasibility, user value and operational impact before scaling solutions.
• Monitor and troubleshoot production AI services, support incident resolution, assess emerging technologies and contribute reusable patterns and operational procedures.

Skills

Python

LLM concepts

Agentic systems

Systems thinking

Problem solving

Education

Bachelor's degree in CS/Engineering/AI or related field

Tools

AWS

Azure

Kubernetes

CI/CD

REST APIs

Vector search

Job description

Our Opportunity

Work Location: Perth - Fortescue's Perth office is located on the traditional lands of the Whadjuk Noongar people.

Roster: Monday to Friday.

As an AI Engineer within Fortescue’s Data & AI Platform team, you will help design, build and operate secure, scalable AI solutions and platform capabilities across machine learning, generative AI, retrieval-augmented generation (RAG) and agentic workflows.

You will translate business problems into production-ready solutions, develop reusable services and integrations, and support end-to-end MLOps and LLMOps. Working closely with data scientists, engineers, architects, cyber security, product and operational teams, you will balance delivery speed with reliability, governance, cost and measurable business value.

Key Responsibilities

• Develop and maintain reusable AI components, APIs, microservices, model-serving capabilities and orchestration patterns.
• Implement machine learning, generative AI and RAG solutions using architectures appropriate to the business need.
• Build capabilities for automated testing, deployment, evaluation, monitoring, cost management and lifecycle management across MLOps and LLMOps.
• Design and implement bounded agentic workflows using approved enterprise data, tools and APIs.
• Apply appropriate patterns for tool calling, context and state management, orchestration, human approval, guardrails and safe fallback behaviour.
• Evaluate and monitor AI and agent workflows for task success, reliability, safety, latency, cost and operational value.
• Integrate AI services and agents into enterprise business workflows in alignment with architecture, cyber security, data governance and responsible AI requirements.
• Build secure integrations using appropriate identity, authorisation, secrets management, least-privilege access and auditability controls.
• Prototype and experiment to validate technical feasibility, user value and operational impact before scaling solutions.
• Monitor and troubleshoot production AI services, support incident resolution, assess emerging technologies and contribute reusable patterns and operational procedures.

Qualifications And Experience

• Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence or a related field, or equivalent practical experience.
• Minimum 2+ years’ experience designing, developing and delivering AI/ML or cloud-native software systems, including solutions from prototype through to production.
• Proficiency in Python, including clean code, testing, API development, debugging and software design fundamentals.
• Practical understanding of machine learning and LLM concepts, model APIs, prompting, structured outputs, embeddings, vector search and RAG.
• Practical experience or strong applied understanding of agentic systems, including tool/function calling, workflow orchestration, context or state management, guardrails and human-in-the-loop controls.
• Experience evaluating AI quality and reliability, monitoring production behaviour, tracing model and tool activity, and managing latency, configuration and cost trade-offs.
• Working knowledge of AWS or Azure, containers, Kubernetes, infrastructure as code, CI/CD, automated testing and observability practices.
• Experience with SQL, data pipelines, object storage, REST APIs, event-driven or microservices architectures and governed enterprise data integration.
• Understanding of authentication, authorisation, secrets management, data privacy, secure tool use, AI risk controls and responsible deployment practices.
• Strong analytical, systems-thinking, problem-solving and communication skills, with the ability to collaborate across disciplines and communicate technical options, risks, costs and outcomes clearly.

About Us

Be part of something big. Fortescue is leading the world with our plan to decarbonise our iron ore operations, projects that harness renewable energy and the development of technology that will change our planet forever.

Our Commitment

Fortescue celebrates individual strengths and team members are encouraged to bring their whole selves to work. Our global workforce drives and promotes an inclusive culture, both within our organisation and throughout the communities we interact with. Diverse backgrounds include First Nations Peoples, people with disabilities, LGBTQ+ community, gender, neurodiverse, cultural diversity, all age groups, and those with an intersectional or multiple diverse characteristics.

We encourage candidates from all backgrounds to apply.

https://fortescue.com/careers