Principal Digital Transformation Specialist

Worley • Edmonton, AB

4d

About The Position

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.

Requirements

• 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 RAG architectures.
• Proven experience designing and implementing 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.

Nice To Haves

• 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.

Responsibilities

• Design and implement AI solutions leveraging Retrieval-Augmented Generation (RAG), Agentic workflows (tool use, orchestration, planning), and 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.
• Lead a rapid MVP-based delivery approach, developing solutions in short cycles (weeks, not months), validating with users using measurable success criteria, and iterating 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.
• 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 standards, specifications, knowledge bases, and 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.
• Partner with engineering and business teams to identify and prioritize high-value AI opportunities.
• Translate business problems into AI system designs, including user interaction patterns, workflow integration approaches, and 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.
• Apply MLOps / LLMOps practices, including CI/CD pipelines, containerization, deployment patterns, monitoring, observability, and performance tracking.
• Define and apply evaluation frameworks for grounding and hallucination risk, accuracy, usability, performance metrics, and 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.
• 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.

Benefits

• We want our people to be energized and empowered to drive sustainable impact.
• 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.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal