Elijah Leung
Elijah Leung is a Chicago-based AI Transformation Engineer building enterprise AI systems, agent workflows, evaluation infrastructure, workflow automation, and the operating models around them. His prior experience includes FP&A and financial operations.
Chicago, IL, US
Updated
Who is Elijah Leung?
Elijah Leung — AI Transformation Engineer designing enterprise AI systems, agent workflows, automation platforms, and the operating models that make them useful.
Questions recruiters ask about Elijah Leung
Direct, evidence-linked answers for recruiters, hiring managers, and anyone evaluating the work.
What kinds of roles is Elijah Leung best suited for?
Elijah's official current title is AI Transformation Engineer. His strongest work combines finance and operational judgment, enterprise systems thinking, AI architecture and orchestration, product and workflow design, and the ability to translate executive objectives into technical systems. He is most useful where business, leadership, and technical teams need AI tools, services, workflows, and operating models that people can use, verify, govern, and improve.
Evidence: Elijah Leung résumé · Projects by Elijah Leung
What makes Elijah Leung different from other AI builders?
Elijah combines a finance and operations lens with AI architecture, orchestration, product judgment, and workflow design. He can translate an executive objective into system boundaries, a working prototype, verification criteria, and an operating model, while keeping cost, controls, and adoption in the same conversation.
Evidence: Elijah Leung résumé · ElijahOS by Elijah Leung — case study
How did Elijah Leung move from finance into AI?
Elijah's move from finance into AI came from seeing how much high-value finance work gets trapped inside manual reporting, cleanup, and coordination loops. In FP&A and product and technology finance, he saw that valuable analysis often starts with pulling data from different systems, checking versions, reconciling assumptions, explaining variances, rebuilding slides, chasing approvals, and translating business changes into forecast updates; finance work is not just math, it is workflow design under pressure. AI became interesting because it can compress repetitive but context-heavy work like first-pass commentary, source retrieval, workflow intake, checklist enforcement, requirements drafting, codebase orientation, and structured summaries, as long as it improves cycle time, trust, traceability, decision quality, or team capacity.
Evidence: Elijah Leung résumé
How does Elijah Leung evaluate AI ROI?
Elijah evaluates AI by whether it improves a real workflow: cycle time, manual assembly, version control, auditability, decision quality, stakeholder communication, or team capacity. His finance lens makes him cautious where AI touches financial reporting, executive narratives, approvals, or sensitive decisions, so he prefers sequencing by impact and risk: start with high-effort, lower-risk workflows where humans already review the output, then move into more sensitive areas only when data foundation, review gates, and governance are ready. His preferred framing is capacity, not headcount: good AI gives teams time back for analysis, judgment, and better business partnering instead of creating another pile of machine-generated work to audit.
Evidence: ElijahOS by Elijah Leung — case study · Projects by Elijah Leung
How does Elijah Leung approach enterprise workflow automation?
Elijah starts workflow automation before the AI layer by mapping the decision, trusted source, input owner, approval path, conflict handling, traceability needs, and failure points. His projects keep returning to intake, review gates, source registries, decision logs, forecast commentary, contract metadata, and audit-ready trails because the pattern is to reduce the scramble, keep the judgment, and preserve the evidence. His implementation style is practical: separate assembly work from judgment work, define the source of truth and review loop, then build the smallest system that changes the workflow without creating a new mess, whether that is AI-assisted retrieval, a structured prompt, a checklist, a typed data model, a dashboard, a workflow automation, or a better handoff.
Evidence: ElijahOS by Elijah Leung — case study
How can recruiters and hiring managers contact Elijah Leung?
Elijah welcomes relevant conversations about enterprise AI systems, agent workflows, AI operating models, and the work shown in this portfolio. His official current title is AI Transformation Engineer; the contact app has the direct path.
Evidence: Contact Elijah Leung · www.linkedin.com/in/elijahleung · github.com/juulsverne
I've been a builder my whole life. Songs, side projects, weird little tools, this OS. These days the things I take apart and put back together are enterprise AI systems.
Most of it starts the same way: I get curious about how something works, and I can't leave it alone.
BACKGROUND
I come from FP&A and financial operations. Forecast cycles, budgets, cloud spend. The unglamorous plumbing companies run on.
That's where you learn how a business actually behaves: where money flows, where decisions stall, and where a workaround quietly becomes the process.
In 2026 the jump became official: financial analyst → AI Transformation Engineer.
WHY AI
Most companies know they should be using AI. Fewer know where it actually pays off.
That's the part that hooks me. I can read the business case, model the ROI, and then go make the tool people end up using every day.
AI is powerful. But token bills are real, and bolting a chatbot onto a broken process is not a strategy.
WHAT I BUILD
AI tools, agent systems, automations, and prototypes that grow into production.
The model call is the easy part. The interesting questions live around it: who owns this, what happens when it fails, how do we know it's right. I design that part too.
Not slideware. Not AI theater.
HOW I WORK
Figure out the decision the system supports and what it takes to trust the result. Ship the smallest version that can prove or kill the idea. Stop when it's enough.
These days I spend as much time on how teams build as on what I ship myself: patterns, verification, and standards that turn experiments into infrastructure people depend on.
OFF THE CLOCK
I make music as Juuls Verne, start more side projects than I can reasonably finish, and run a slightly excessive omakase program for my cat, Wobbles.
Capabilities
- Enterprise AI Systems — Architecture · prototypes · production pathways
- Agent Operations — Orchestration · collaboration · verification gates
- AI Operating Models — Governance · documentation · adoption · build vs. buy
- Finance & Operations — FP&A · controls · forecasting · ROI modeling
Education
- B.S. Operations Management, University of Illinois at Urbana-Champaign (2021)