Lead AI Engineer

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 per