EPC Process Improvement Manager

Terabase Energy•Berkeley, CA

1d•Onsite

About The Position

We are seeking a Process Improvement Manager to sit at the intersection of EPC domain expertise and AI system design. This role is responsible for translating complex, real-world EPC and construction workflows—particularly engineering, estimating, procurement, and construction item processes—into precise specifications that AI engineers can implement as high-impact agents. You will work directly with construction professionals (estimators, engineers, project managers) and software engineers to map how work actually gets done today, identify bottlenecks on the critical path, and convert those insights into clear, testable AI agent specifications that compress schedules and reduce execution risk. This is not a theoretical process role—it is hands-on, workflow-level, and outcome-driven.

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, 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 with a focus on conceptual engineering, estimating, detailed design, long-lead procurement, contracting and construction.
Produce very granular process maps, swim-lane diagrams, and value stream maps that reflect real execution—not idealized procedures.
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 immediate value.
Prioritize opportunities based on schedule compression, feasibility, risk reduction, and scalability (e.g., long-lead release acceleration, engineering iteration reduction).
Partner with technical teams to define short-term vs. long-term AI roadmaps aligned to EPC execution realities.
Translate business workflows into AI agent specifications, clearly defining: Inputs (documents, data, constraints), Outputs (decisions, deliverables, recommendations), Triggers and 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 actually work under schedule pressure.
Iterate specifications based on field feedback to ensure adoption and measurable impact.