Introduction

Brunel has partnered with an Energy & Resources industry leader to offer an exciting contract opportunity for an Senior Full Stack AI Engineer. This role is well suited to someone with strong software and ML engineering experience, who is looking to deepen their expertise across ML, GenAI, and MLOps platforms.

What are you going to do

The Data & AI Platform team builds the foundational capabilities that enable scalable, secure, and high-impact AI solutions. We design and operate the platforms, components, and workflows that support machine learning, GenAI, data-driven optimisation, and enterprise automation.

As an AI Engineer / Developer, you will help build and operate our AI platform-supporting traditional ML, LLM-based applications, orchestration frameworks, and end-to-end MLOps/LLM Ops processes. You'll work with data scientists, architects, and product teams to deliver reliable, integrated, and value-focused AI solutions across the business.

Key Responsibilities

AI Platform Engineering and Development

  • Lead design, build, and deployment of end-to-end applications, covering frontend, backend, and integration with software and AI systems.
  • Independently design and implement scalable web applications, including responsive user interfaces, APIs, and secure backend services.
  • Architect and develop reusable, robust, and enterprise-grade AI and UI components, frontend frameworks, APIs, microservices, backend services, and frameworks aligned with Fortescue's architectural standards.
  • Ensure seamless integration between user-facing applications and AI-powered backend services.
  • Maintain best practice guidelines for frontend frameworks, backend services, and full-stack deployments, not just AI.

Enterprise Solution Integration

  • Proactively collaborate with architects, engineers, and data scientists to ensure seamless integration of advanced AI solutions into business-critical workflows.
  • Ensure developed solutions consistently adhere to established architecture patterns, security protocols, and data governance standards.
  • Develop and maintain detailed reference architectures, technical documentation, and best practice guidelines for AI solution development and deployment.
  • Explore and prototype modern frontend and backend frameworks (React, Angular, Vue, Node.js, etc.) alongside AI technologies.

Technical Strategy and Innovation

  • Lead and execute technical prototyping, experimentation, and evaluation of emerging AI technologies, exploring their potential operational application at Fortescue.
  • Influence the AI platform roadmap, balancing application usability, performance, and AI/ML innovation.

Leadership, Collaboration and Mentorship

  • Mentor junior team members, enhancing engineering capability, and driving adoption of best practices across the team.
  • Act as a key point of technical expertise within cross-functional agile teams, facilitating knowledge sharing, and ensuring alignment with business requirements.

Essential skills and knowledge 

  • Bachelor's or Masters degree in Computer Science, Engineering, Artificial Intelligence, or related field.
  • Minimum 4+ years of experience designing, developing, and delivering robust full-stack web applications alongside AI/ML systems, including extensive experience in cloud-native environments.
  • Recent practical experience in frontend and backend frameworks (React, Angular, Node.js, Express, etc.).
  • Proficient in Python and JavaScript/TypeScript (essential for full stack), C# and .NET. Familiarity with frameworks such as React, Angular, or Vue for frontend; Node.js or Express for backend.
  • Strong experience with HTML, CSS, JavaScript/TypeScript, and modern UI libraries/frameworks.
  • Experience in building secure, scalable RESTful or GraphQL APIs and microservices.
  • Strong knowledge of relational (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB) databases.
  • Testing & Quality: Experience with automated testing frameworks for frontend (e.g Jest, Cypress) and backend (e.g. Mocha, PyTest).
  • Cloud & DevOps: Strong working knowledge of cloud environments (AWS or Azure), Docker, Kubernetes, Terraform, advanced CI/CD automation, and observability practices.
  • Data & Platform Engineering: Extensive experience integrating platforms and data services including Snowflake, Kafka, AWS S3, REST APIs, microservices, and knowledge graph technologies.

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