Lead AI Engineer

About us

Worley is a global company of energy chemicals and resources experts headquartered in Australia. We partner with our customers to deliver projects and create value across the life of their assets. We specialize in consulting engineering procurement and construction across the project lifecycle with services extending through to operations and decommissioning. Leveraging extensive experience and AI-enabled delivery we support customers in navigating complexity as they meet todays needs and transition to more sustainable solutions.

Role Summary:

Our Lead AI Engineer role plays a key role in enabling Worleys digital transformation by designing prototyping and scaling AI-powered solutions that enhance engineering workflows automate knowledge-driven processes and unlock measurable business value across the project delivery lifecycle.

This role bridges deep engineering domain expertise and advanced AI/ML capabilities translating complex engineering data (e.g. lifecycle data technical documentation and standards) into intelligent production-ready systems.

Key Responsibilities:

1) AI Solution Design & Architecture

- Design and implement AI solutions leveraging:

o Retrieval-Augmented Generation (RAG)

o Agentic workflows (tool use orchestration planning)

o Structured outputs (schemas JSON function calling)

- Define reusable architecture patterns tailored to engineering use cases (e.g. PEP MDR technical documentation)

- Recommend model strategies aligned to cost performance and security constraints

- Ensure solutions remain model-agnostic and adaptable to evolving enterprise platforms

- Partner with Enterprise Architecture to align with standards integration patterns and security requirements

2) Rapid MVP Development Scaling Delivery

- Lead a rapid MVP-based delivery approach:

o Develop solutions in short cycles (weeks not months)

o Validate with users using measurable success criteria

o Iterate based on feedback

- Transition validated solutions from Incubator environments to scalable enterprise architectures

- Optimize solutions across performance latency cost and reliability

- Support structured handoff to production teams with clear architecture documentation and scaling guidance

3) Engineering Workflow Transformation

- Apply AI to complex engineering datasets (e.g. equipment lifecycle data technical documentation simulation-informed datasets) to improve decision-making and automation

- Develop AI-powered solutions that improve engineering workflows using Worley data including:

o Standards specifications and knowledge bases

o Project documentation (e.g. PEPs MDRs)

- Build and deploy RAG-based applications to generate validate and augment engineering outputs

- Design structured outputs and human-in-the-loop workflows for high-confidence engineering use cases

- Contribute to reusable datasets and knowledge systems that support scalable AI adoption

- Translate engineering lifecycle challenges into practical deployable AI-enabled solutions

4) Product Value and Business Enablement

- Partner with engineering and business teams to identify and prioritize high-value AI opportunities

- Translate business problems into AI system designs including:

o User interaction patterns

o Workflow integration approaches

o Measurable value frameworks (time savings quality improvements productivity gains)

- Support adoption of AI solutions by embedding them into engineering workflows

- Contribute to broader digital transformation initiatives

5) MLOps Evaluation and Responsible AI

- Apply MLOps / LLMOps practices including:

o CI/CD pipelines containerization and deployment patterns

o Monitoring observability and performance tracking

- Define and apply evaluation frameworks:

o Grounding and hallucination risk

o Accuracy usability and performance metrics

o Model performance monitoring and drift awareness

- Ensure transparency auditability and traceability of AI outputs

- Align solutions with enterprise security data governance and Responsible AI principles

6) Stakeholder Collaboration & Mentorship

- Collaborate with cross-functional teams (Engineering Data Architecture Security)

- Present insights prototypes and outcomes to stakeholders and leadership

- Mentor team members on AI solution design prompting techniques and architecture approaches

- Support adoption and scaling of AI capabilities across engineering teams

Skills & Experience Required:

- Bachelors degree in Engineering Data Science Computer Science or related discipline

- 4 years of experience delivering AI/ML or GenAI solutions in production or near-production environments

- Proven experience designing and implementing:

o RAG architectures

o Agentic workflows and AI copilots

- Strong proficiency in Python and modern AI frameworks (e.g. PyTorch LangChain or equivalent)

- Experience with cloud platforms and MLOps practices (CI/CD Docker MLflow or equivalent)

- Solid understanding of system architecture patterns (APIs microservices event-driven systems)

- Proven ability to translate complex engineering or business problems into scalable AI solutions with measurable impact

- Demonstrated ability to deliver AI solutions that drive measurable improvements in engineering productivity quality or efficiency

- Strong communication skills with ability to work across technical and business teams

Skills & Experience Preferred:

- Masters degree in AI-related discipline

- Experience applying AI within engineering energy or industrial environments

- Knowledge of engineering workflows and project delivery processes (e.g. PEPs MDRs)

- Experience integrating AI into enterprise platforms (e.g. SharePoint APIs data platforms)

- Exposure to AI evaluation frameworks LLMOps or governance practices

- Experience with computer vision or advanced analytics

Moving forward together

We want our people to be energized and empowered to drive sustainable impact. So our focus is on a values-inspired culture that unlocks brilliance through belonging connection and innovation. Were building a diverse inclusive and respectful workplace. Creating a space where everyone feels they belong can be themselves and are heard.

And were not just talking about it; were doing it. Were reskilling our people leveraging transferable skills and supporting the transition of our workforce to become experts in todays low carbon energy infrastructure and technology. Whatever your ambition theres a path for you here.

And theres no barrier to your potential career success. Join us to broaden your horizons explore diverse opportunities and be part of delivering sustainable change.