Business Analyst and AI Automation Engineer

About the Company

We are looking for a practical, execution-focused process analyst and AI Engineer to help identify where AI can reduce manual coordination work, improve visibility, and support better project execution outcomes.

About the Role

This role will embed with the Project Management team as both an AI workflow lead and an early AI pilot user/tester. The person in this role will identify high-friction workflows, evaluate where AI can realistically help, pilot and test AI-enabled solutions in live project-delivery contexts, and help translate user feedback into scalable process and tool improvements. This is an ideal role for someone who combines:

• strong understanding of project-delivery workflows,
• comfort with AI and workflow automation tools,
• practical judgment about business value and risk,
• and the ability to help teams adopt new ways of working.

The ideal candidate can distinguish between an interesting AI demo and a truly useful workflow in the power plant build environment.

Responsibilities

• Embed with Project Managers and Project Directors to understand day-to-day coordination pain points across engineering, procurement, logistics, construction, and closeout workflows.
• Map the current project management environment and identify where manual handoffs, data silos, status-chasing, and repetitive coordination tasks create drag on team productivity.
• Identify and prioritize the highest-value AI and automation opportunities based on business value, user pain, technical feasibility, and adoption likelihood.
• Design, configure, pilot, and refine AI-assisted workflows for project coordination use cases such as:
• document comment triage and routing,
• meeting follow-up and action item capture,
• cross-system status synthesis,
• risk and issue flagging,
• overdue action tracking,
• coordination handoff support across functions.
• Serve as an embedded AI pilot user and design partner, testing new tools and capabilities against real project-delivery workflows before wider rollout.
• Define and execute lightweight pilots and user acceptance criteria for AI-enabled workflows, including usefulness, reliability, consistency, workflow fit, and operational risk.
• Capture structured feedback on AI behavior, including failure modes, trust barriers, edge cases, usability issues, and opportunities for improvement.
• Partner with digital, product, and business teams to refine prompts, workflow logic, interfaces, and governance based on end-user testing.
• Document workflows and pilots clearly, including user intent, configuration logic, triggers, exception handling, escalation paths, assumptions, and limitations.
• Develop clear Standard Operating Procedures, prompt libraries, and usage guidance that project teams can use independently.
• Design and deliver hands-on training that helps project teams use, test, and trust AI-assisted workflows.
• Establish baselines and measure impact in practical terms, including time saved, cycle time reduced, manual steps eliminated, workflow fit, and adoption.
• Help define where human review is required and where AI should not be used in sensitive, contractual, safety-related, or customer-facing workflows.

Qualifications

• Strong understanding of how project managers and project directors work in complex industrial, EPC, energy, or power-project environments.
• 7+ years of experience in workflow optimization, digital transformation, AI-enabled process improvement, enterprise systems implementation, or project operations improvement.
• Hands-on experience designing, piloting, configuring, testing, or deploying AI-enabled or automated workflows in an enterprise environment.
• Experience working with project management, collaboration, or workflow tools to improve process execution, including configuration, testing, rollout, or adoption of AI-enabled capabilities.
• Ability to assess workflow dependencies, data movement, and integration needs across a multi-tool environment and identify practical implementation tradeoffs.
• Familiarity with Microsoft 365 ecosystem tools, especially Teams, SharePoint, Outlook, and OneDrive.
• Strong communication skills, including the ability to explain technical behavior, AI limitations, and workflow changes in plain language for non-technical users and leadership.
• Ability to work effectively across project, digital, and business teams in a fast-moving, evolving environment.

Required Skills

• Experience in energy, utility, EPC, heavy industrial, or power generation environments; gas power or plant construction/project execution experience strongly preferred.
• Familiarity with LLM-based tools and/or APIs such as OpenAI, Anthropic, or similar.
• Experience with prompt design, lightweight workflow experimentation, or integrating AI-generated outputs into broader workflows.
• Experience working with proprietary or homegrown enterprise platforms, including environments with limited documentation or inconsistent data.
• Familiarity with project execution and engineering tools such as Primavera, SAP project modules, Documentum, Aconex, or similar systems.
• Experience with workflow automation or low-code platforms such as Microsoft Power Automate, Make, Zapier, Power Apps, Retool, Glide, or similar tools.
• Experience supporting AI adoption programs, user enablement, governance, or communities of practice.

Preferred Skills

• You are naturally curious and practical, with a strong bias toward solving real workflow problems.
• You are comfortable testing emerging tools in live operating environments and giving actionable feedback.
• You know how to evaluate AI outputs critically and determine when they are useful, when they need human review, and when they are not fit for purpose.
• You can build credibility quickly with experienced project teams.
• You are able to balance experimentation with operational discipline.
• You can turn ambiguous opportunities into structured pilots and measurable outcomes.

Pay range and compensation package

Success in this role will include:

• Identifying high-value AI use cases grounded in real project management pain points.
• Running practical pilots that are measurable and aligned to project-delivery realities.
• Improving team confidence in AI tools through strong testing, documentation, and training.
• Reducing manual coordination burden without increasing operational risk.
• Providing leadership with credible recommendations on what should scale, what should be improved, and what should not