Lead AI Engineer

Job Description - Lead AI Engineer (HOU04DG)Management Level:Manager:

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 today's needs and transition to more sustainable solutions.

Role Summary:

Our Lead AI Engineer role plays a key role in enabling Worley’s 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 (., lifecycle data, technical documentation, and standards) into intelligent, production-ready systems.

Key Responsibilities:

1) AI Solution Design & Architecture

-Design and implement AI solutions leveraging:

oRetrieval-Augmented Generation (RAG)

oAgentic workflows (tool use, orchestration, planning)

oStructured outputs (schemas, JSON, function calling)

-Define reusable architecture patterns tailored to engineering use cases (., 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:

oDevelop solutions in short cycles (weeks, not months)

oValidate with users using measurable success criteria

oIterate 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 (., 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:

oStandards, specifications, and knowledge bases

oProject documentation (., 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:

oUser interaction patterns

oWorkflow integration approaches

oMeasurable 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:

oCI/CD pipelines, containerization, and deployment patterns

oMonitoring, observability, and performance tracking

-Define and apply evaluation frameworks:

oGrounding and hallucination risk

oAccuracy, usability, and performance metrics

oModel 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:

-Bachelor’s 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:

oRAG architectures

oAgentic workflows and AI copilots

-Strong proficiency in Python and modern AI frameworks (., 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 (., PEPs, MDRs)

-Experience integrating AI into enterprise platforms (., 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. We're building a diverse, inclusive and respectful workplace. Creating a space where everyone feels they belong, can be themselves, and are heard.

And we're not just talking about it; we're doing it. We're reskilling our people, leveraging transferable skills, and supporting the transition of our workforce to become experts in today's low carbon energy infrastructure and technology. Whatever your ambition, there's a path for you here.

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