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 (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:

- 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:

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