Data Engineer

Solve Intelligence is hiring a Data Engineer in London, United Kingdom. Level rates it ; you can apply on Solve Intelligence.

Level's read on this job

On Level's scale, this data engineer role is Works on AI ●●●○: the daily work is on AI products, without building the model.
The posting mentions visa sponsorship.
The posting mentions pgvector, Python and SQL among its tools and skills.
Level classes it as a mid-level role.
Solve Intelligence has 16 other open roles on Level.

AI in this role

Build ingestion, processing, and vector search systems to power AI-driven IP intelligence products.

pgvectorpythonsql

data-pipelinessearch-systemsvector-searchdatabase-design

💡 About Us

We're the fastest-growing startup transforming the IP industry.

Traction: 20-30% MoM revenue growth; selling to 700+ global IP teams (DLA Piper, tech boutiques, and global enterprises).
Proven Value: Users report 50-90% efficiency gains using our AI platform.
Backing: Recently featured in Sifted following our $40M Series B announcement, bringing our total funding to $55M from elite investors including Y Combinator, 20VC, Visionaries, Microsoft and Thomson Reuters.

🏗️ About the role

We’re hiring a data engineer to build the ingestion and search systems behind Solve Intelligence’s AI products.

Our sources include global patent literature, case law, technical standards and contributions, scientific databases, academic papers, and content from across the web. The data spans structured records, documents, images, audio and video. You’ll work across bulk ingestion and on-demand retrieval, making this information searchable and useful in our products

You’ll own systems from source acquisition through to serving queries. The work includes:

Large-scale ingestion. Build and operate high-throughput, resumable pipelines for large datasets, with efficient incremental updates, monitoring and recovery from failures.
Document processing and data quality. Extract useful content from complex documents and other formats. Handle malformed records and changing schemas, and validate outputs while preserving structure and metadata.
Search and serving. Build keyword, vector and structured search, and design schemas, indexes and partitioning for fast queries over tens to hundreds of millions of records.
Connecting information across sources. Link patents, scientific records and supporting documents, preserve dates and versions, and make results traceable to their original sources.
Performance engineering. Profile parsing, ingestion, database builds and queries throughout development, testing against representative datasets at realistic scale. Diagnose CPU, memory and storage I/O bottlenecks, and tune jobs and infrastructure for throughput, latency and cost.

You’ll work closely with our AI researchers and product engineers, with substantial freedom to choose the approach and build the systems yourself.

🛠️ What you bring

Must haves:

Strong Python and SQL, with experience designing and operating production databases.
Solid experience building and operating production data pipelines over large, messy datasets.
Expertise with running search systems over large document collections.
End-to-end ownership from raw data to user-facing functionality.
A good understanding of schema design, indexing and query optimisation.
A track record of diagnosing and fixing performance bottlenecks in live systems through profiling and measurement.

Nice to Have:

Experience with PostgreSQL/pgvector, OpenSearch (or Elasticsearch), Spark/Delta Lake, AWS, NoSQL databases, or Rust/C++ is useful.

👋 The Founders

You'll partner with a founding team of AI PhDs and elite systems engineers:

Sanj (CRO): PhD in AI (Gatsby Unit, UCL), ex-Huawei R&D, former lead at Magic Carpet AI (acquired).
Chris (CEO): PhD in AI (UCL), published researcher, ex-Dyson and Alan Turing Institute.
Angus (CTO): MEng Computer Science, ex-Qualcomm and Coremont (Brevan Howard).

What we offer

Competitive Salary + Significant Equity: We want you to have true ownership in the success of the company.
Founding Impact: You'll have a direct hand in how we build out the data infrastructure the rest of the product depends on.
Support: Full visa sponsorship and private medical insurance.
The Environment: Free meals and a seat at the table with an incredibly smart, ambitious team.

How we rate this

Data Engineer at Solve Intelligence rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Data Engineer is an open job at Solve Intelligence.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in London.
The job was posted on September 16, 2026.
Seniority: mid.
Applications are handled by Solve Intelligence. Apply on Solve Intelligence

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Data PipelinesSearch SystemsVector SearchDatabase DesignpgvectorPythonSQL

Also common in data engineer jobs at this level

Machine learning Databricks Azure Airflow Snowflake Google Cloud

Pay

Typical data engineer job: $148k

This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs

Questions you could be asked

Tell me about a project where data pipelines was part of your work. What did you do?
Tell me about a project where search systems was part of your work. What did you do?
Tell me about a project where vector search was part of your work. What did you do?
Tell me about a project where database design was part of your work. What did you do?
Walk me through how you've used pgvector in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Data Pipelines, Search Systems, Vector Search, Database Design, and pgvector. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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