Job Description

Description

We're building

Orbital

, an industrial AI system that lives inside refineries and upstream assets, serving real-time insights to operators, technologists, and engineers. As a

Forward Deployed Full Stack Engineer

, you'll make Orbital usable on the ground: building interfaces, APIs, and integration layers that bring AI outputs directly into operational workflows.




This isn't a typical web dev role. You'll work across

back-end services, APIs, and industrial integrations

, while also shaping

front-end interfaces

that can survive operator control rooms and engineering workflows. You'll be customer-facing: working directly with site teams, adapting features in real time, and making sure the system sticks in production.




You won't just productionise models, you'll

install Orbital on customer sites

, integrate with live historian and process data pipelines, and ensure the system runs inside customer IT/OT networks.

Location:

Whilst you will be based in the Europe and or eligible to work here - this role will involve travel to other locations in India & USA.

Core Responsibilities

Application Development

Build and maintain

front-end dashboards and interfaces

for refinery operators, technologists, and engineers. Develop

back-end APIs and services

that integrate Orbital's AI outputs into customer systems. Ensure applications are secure, reliable, and performant in both cloud and on-prem environments.

Microservices & Integration

Develop services as

containerised microservices

, orchestrated in Kubernetes/EKS. Connect front-end and back-end layers with message brokers (Kafka, RabbitMQ) and API gateways. Integrate with industrial data sources (historians, LIMS, OPC UA, IoT feeds).

Forward Deployment & Customer Adaptation

Deploy full-stack applications in customer on-premise networks. Work with process engineers, IT/OT, and operations teams to customise UI/UX for their workflows. Debug and iterate features live in the field, ensuring adoption and usability.

Software Engineering Best Practices

Write clean, modular, and testable code across front-end and back-end. Set up CI/CD pipelines for fast iteration and deployment. Collaborate closely with product owners and ML engineers to align UI with model capabilities. Adapt UX to site-specific workflows (control room, process engineering, production tech teams). Collaborate with ML Engineers to surface inference + RCA results in usable, real-time dashboards.

Customer Facing

Deploy AI microservices in

customer on-prem

(often air-gapped or tightly firewalled)

/ our cloud clusters.

Connect Orbital pipelines to

customer historians, OPC UA servers, IoT feeds, and unstructured data sources

. Build data ingestion flows tailored to each site, ensuring schema, tagging, and drift handling are robust. Work with customer IT/OT to manage network, security, and performance constraints.

Requirements

Strong proficiency in

JavaScript/TypeScript (React, Node.js)

and back-end frameworks (FastAPI, Express, Django). Solid working knowledge of

Python

for scripting, APIs, and data integration. Experience building

containerised microservices

(Docker, Kubernetes/EKS). Familiarity with

message brokers

(Kafka, RabbitMQ). Proficiency with

Linux environments

(deployment, debugging, performance tuning). Hands-on experience with

AWS (EKS, S3, IAM, CloudWatch, etc.)

. Bonus: exposure to

time-series/industrial data

and operator-facing dashboards. Comfort working in

forward-deployed, on-premise customer environments

.

What Success Looks Like

Orbital has

user-facing dashboards and APIs

that operators actually use in production. Applications run reliably in

customer on-premise environments

. Features are rapidly iterated based on

live user feedback

from engineers in the field. * Full-stack code integrates seamlessly with Orbital's AI/ML microservices.