About Applied Computing
Founded in 2024, Applied Computing is on a mission to deliver sustainable abundance for a growing planet through AI built for the energy industry.
Energy is an enduring necessity it powers our planet. Yet its complexity has kept the industry tethered to legacy systems, with critical decisions made on less than 10% of available data.
We built Orbital to change that. Orbital is a Multi-Foundation AI system that enables energy companies to finally trust AI in the control room, harnessing 100% of their data and optimising in real time for any metric. The result: faster decisions, safer operations, and higher performance.
In 2025, we raised $10.7 million in seed funding one of the largest Seed rounds for an AI company in the UK and we are just getting started.
We’re building the data backbone for Orbital, an industrial AI system that ingests and learns from complex refinery and process data in real time. As our Data Engineer, you’ll architect and maintain pipelines that make high-frequency time-series, lab, and historian data into a scalable
Lakehouse architecture
, usable for both deep learning models and real-time LLMs. You’ll be working across
AWS (EKS, S3, EBS, KMS, CloudWatch)
and
Databricks/PySpark
, ensuring data is contextualised, synchronised, and optimised for both deep learning models and real-time LLM workloads.
This isn’t a traditional ETL role, you’ll be solving problems at the intersection of
control systems, industrial data engineering, and AI enablement
.
Technical Requirements
Core Responsibilities
1. Ingest & Contextualise Data
- Ingest from
OPC UA servers, process historians, IoT sensors, LIMS systems, alarms/events, and P&IDs
.
- Map signals to their physical processes (tags, units, hierarchies) for interpretability in AI pipelines.
2. Data Movement & Accessibility
- Build pipelines that handle
real-time streaming and batch ingestion
into the Lakehouse.
- Manage
synchronisation between historian archives, unstructured files, and AWS storage (S3/EBS)
.
- Orchestrate
DatabricksLakeflow/Connectors
for integrating data into Lakebase/Lakehouse.
- Handle secure, high-throughput transfers between historian archives and sandbox/live environments.
3. Change Tracking & Integrity
- Detect and manage schema changes, signal drift, and inconsistencies acrosstime.
- Implement lineage and audit trails across Spark/Databricks and AWS pipelines.
4. Data Preparation for AI
- Build andmaintaindual pipelines:
- Training
→ large-scale historical data prep for time-series + LLM training.
- Inference
→ low-latency, real-time pipelines for anomaly detection, optimisation, and LLM search.
- Support heterogeneous AI workloads (time-series forecasting and retrieval-augmented LLMs).
5. Database Performance & Optimisation
- Tune PostgreSQLand sparkfor high-throughput time-series workloads (partitioning, indexing, query optimisation).
- Optimise pipelines for both fast analytical queries and high-efficiency model training.
- Deploy and manage data pipelines in
AWS EKS (Kubernetes)
with persistent
EBS-backed storage
.
What Success Looks Like
- Live data streams are contextualised,queryable, and AI-ready.
- Schema changes and signal drift are detected and handled without breaking downstream workflows.
- Training and inference pipelines run smoothly in parallel, optimised for scale and latency.
What we offer
- Competitive compensation plus equity
- Work from home setup allowance
- Private Medical
- Learning and conferencing allowances
- More to come
