EPC Process Improvement Manager

Terabase Energy•Berkeley, CA

20h•Onsite

About The Position

At Terabase Energy, we are developing an interconnected software and construction automation platform to drive innovation and cost reduction in large-scale solar energy. We are seeking a Process Improvement Manager to bridge EPC domain expertise and AI system design. This role involves translating complex EPC and construction workflows into precise specifications for AI engineers to implement as high-impact agents. You will collaborate with construction professionals and software engineers to map current workflows, identify bottlenecks, and create clear, testable AI agent specifications to compress schedules and reduce execution risk. This is a hands-on, workflow-level, and outcome-driven role.

Requirements

Bachelor's degree in Engineering or equivalent
5+ years' experience in business process design, operations analysis, systems engineering, or EPC project delivery
Strong working knowledge of EPC workflows, particularly Engineering (conceptual → detailed design → construction drawings), Procurement and contracting strategy, and Schedule and dependency management
Proven ability to create clear, structured process documentation from ambiguous real-world inputs
Expert with process mapping tools (Miro, Lucidchart, Visio, or equivalent) and presentation tools (Powerpoint, Prezi, gamma.ai or equivalent)
Excellent communication skills—able to translate seamlessly between domain experts and technical teams

Nice To Haves

Familiarity with Lean, Six Sigma, BPMN, or value stream mapping methodologies
Experience working with or alongside software, data, or AI teams
Exposure to data center, power generation, or large industrial EPC projects
Comfort operating in environments where processes are evolving and not fully documented

Responsibilities

Decompose end-to-end EPC workflows, focusing on conceptual engineering, estimating, detailed design, long-lead procurement, contracting, and construction.
Produce granular process maps, swim-lane diagrams, and value stream maps reflecting real execution.
Identify handoffs, rework loops, decision gates, data dependencies, and critical path constraints across disciplines.
Maintain a system-level view of how engineering outputs unlock procurement and construction activities.
Analyze mapped workflows to pinpoint schedule-critical and labor-intensive tasks where AI can deliver value.
Prioritize opportunities based on schedule compression, feasibility, risk reduction, and scalability.
Partner with technical teams to define short-term and long-term AI roadmaps aligned with EPC execution realities.
Translate business workflows into AI agent specifications, defining inputs, outputs, triggers, dependencies, success metrics, and acceptance criteria.
Ensure specifications are detailed enough for AI engineers to implement without domain guesswork.
Explicitly document edge cases, failure modes, and human-in-the-loop requirements.
Review agent concepts and prototypes with stakeholders.
Validate that agent behavior aligns with how EPC teams work under schedule pressure.
Iterate specifications based on field feedback to ensure adoption and measurable impact.