UI/UX Fullstack Engineer

About the role

We're looking for a Senior Fullstack UI/UX Engineer who's passionate about building powerful, intuitive, data-rich enterprise applications, and who wants to put that passion to work in manufacturing and industrial settings — helping frontline teams solve their most concrete pain points: data they can't make sense of, problems caught too late, and decisions made without enough to go on.

You'll work closely with data engineers, data scientists, product managers, and domain experts, turning the data data engineering has cleaned up and the insight data science has produced into a genuinely usable, intelligent user experience — making complex data exploration, cross-system information integration, and role-aware workflows (from frontline operators to management) feel intuitive to use. Inspired by platforms like C3.ai, Aveva, AspenTech, Palantir, and Siemens, your work will turn industrial AI and analytics into tools that operators, engineers, and managers across a multi-layered organization can pick up immediately and actually use to solve problems.

We want you to own this product end-to-end: design the interface, build the frontend (React), build the backend behind it, and work closely with data engineers and data scientists to make sure what the interface shows genuinely reflects what the data and models can actually do. Small team, high ownership — you'll help define the spec, not just implement someone else's.

What you'll build

Real-time data overviews — making all kinds of frontline data explorable in near real time.
AI-driven insight and alert triage — surfacing what's unusual and why, with prioritized, actionable suggestions.
A unified equipment/production-line health view — pulling cross-system information together so it's clear at a glance whether things are on track.
A conversational AI interaction layer — letting users ask questions of their own data in plain language and get decision-support suggestions back.
The API and data-integration layer behind all of it — connecting data engineering's data, data science's model outputs, and all the other frontline information.
The design itself — turning complex, constraint-heavy industrial data into something non-technical frontline staff can actually use.

Core requirements

Frontend

Strong React skills; comfortable designing and owning component architecture from scratch.
Experience with data-dense interfaces: dashboards, timelines, relational/network visualizations, real-time updating views.
Ability to design AI-assisted interaction patterns — conversational interfaces, inline suggestions, and human-in-the-loop confirmation for AI-driven actions.
Genuine UI/UX design ability — wireframing, interaction design, simplifying complex data for non-expert users. Portfolio required.
Testing discipline: writing tests from a spec, not just against your own implementation.


Backend

Solid API design and data-contract/schema skills, including evolving versions without breaking existing consumers.
Ability to design an aggregation layer that pulls documents, sensor data, model outputs, and schedules into one coherent view.
Comfort integrating LLM-generated responses into the application (streaming output, grounding answers in the right data, handling uncertainty gracefully).

General

Full-stack fluency: able to independently trace a feature from the interface, through the API, to the data layer, without needing a specialist at every step.
Comfortable working from written specs/contracts and producing clean handover documentation.
Strong asynchronous, written communication.


Strongly preferred (bonus — not required to have all of these)

Data engineering experience: pipelines, ingesting sensor/industrial data, time-series data handling.
Data science/ML literacy: enough to discuss model outputs, confidence levels, and data drift with data scientists, and represent them honestly in the UI.
Operations research/scheduling familiarity: constraint-based planning concepts (lead times, changeovers, capacity feasibility).
Manufacturing/industrial domain experience: prior exposure to frontline operations or production planning.
Basic awareness of graph-based ML: enough to work productively alongside the team building the underlying model.
Observability/dashboarding experience, even informal.
Prior work building on top of LLMs/agentic products: prompt/context design, evaluating model outputs, designing for trust and explainability.

How you'll work

You'll work closely with data engineers (on data contracts and pipeline outputs) and data scientists (on what the models can actually promise), translating both into interfaces frontline staff can trust and act on directly. You'll move between design, frontend, and backend within the same week.