AI Engineer

About us

Mainspring is a UK AI implementation firm helping engineering and manufacturing businesses put AI to work on their own terms. We build governed knowledge layers on each client's records, systems and approvals, and use them to bring scattered information together, automate repetitive technical work, and monitor machines and sites.

Our solutions are model-agnostic, cost-modelled before we build, and deployed where the client's data needs to sit. We are practical and evidence-led, and we are honest with clients when AI is not the answer.

Our team has founded and sold software companies, designed large-scale industrial machinery and managed manufacturing partners. Our projects span carbon removal, agriculture, field engineering and energy consultancy.

Joining Mainspring means working on real problems for real clients. You will work alongside engineers, consultants and compliance specialists, and your work will go live in the businesses we serve and make a measurable difference there.

The role

You take a design that has already been scoped and agreed, and turn it into something that works in a client's business. The model, the tools it calls, the instructions it follows, and the logic that lets it act and correct itself when it gets something wrong.

No two builds look the same. One client needs a calculator that qualifies their incoming leads. Another needs an assistant that can answer questions from twenty years of specification documents, with permissions, approvals and a record an auditor could follow. You will work from patterns we have proven before where they fit, and build something new where they do not.

You will work close to the client rather than at a distance from them. Most of your week is hands-on technical work, but a real part of it is talking to their engineers, operators and technical managers to find out what their data and systems will actually support. The gap between what a system is supposed to do and what the client's reality allows is where these projects are won or lost.

What you build goes into live production. You will be judged on whether it works for the people using it.

The role is based in our Kuala Lumpur office, working alongside the UK team and with clients across UK engineering and manufacturing.

What you'll be doing

• Build agents, assistants and tools against the agreed design, from first pilot through to production.
• Write the prompts, tool integrations and decision logic, including the validation and error handling that stop a system failing quietly.
• Connect what you build to the client's systems, APIs, databases and industrial data sources.
• Build as much of the knowledge, permission and audit layer as the design calls for. Sometimes that is one trusted source. Sometimes it is everything the organisation knows.
• Prove it works. You build and run the evaluation evidence for your own system, so the decision to go live rests on numbers rather than a demo.
• Pick the model for each job within the agreed strategy, weighing capability, cost, speed and where the data is allowed to sit.
• Keep running costs inside the estimate, put the agreed limits in place, and report what it is actually costing.
• Deploy into whatever environment the client's data requires, whether that is cloud, a private environment or on premises.
• Get pilots in front of real users quickly, then iterate on what they tell you.
• Say something early when the build shows the design was wrong. Technical problems go to the architect, anything that changes what we promised goes to the delivery manager.
• Hand back what worked, so the next engagement starts further along than yours did.

This role does not own the design, the budget, the client relationship, or the decision to go live.

Who we're looking for

Key competencies

• You can explain a technical trade-off to someone who is not technical, and be understood.
• You are comfortable in front of clients, and comfortable when the requirements shift under you.
• You can take a problem apart and work out which bit actually matters.
• You can own a piece of delivery end to end without being managed through it.
• You ship, then improve. But you care about what you leave behind for the next person to maintain.
• You would rather be measured on whether the thing worked than on how much you built.
• You work well with shared standards, and you contribute back to them. Our team spans two time zones, so this matters.

Essential

• Five or more years in software or data engineering.
• Solid engineering fundamentals, usually in Python, including testing and working with Git.
• Real hands-on experience with large language models: prompting, retrieval, and at least one agent framework.
• You have evaluated an LLM system properly. You know how to design test cases, find the ways it fails, and show that the output can be trusted in production.
• Experience building production data pipelines and ETL or ELT processes.
• Comfortable with REST APIs, webhooks and third-party services.
• Experience with at least one of Azure, AWS or Google Cloud.

Desirable

• You have put AI systems into production before, including the monitoring and observability that keeps them honest.
• Familiarity with retrieval-augmented generation, vector databases and knowledge-base design.
• Experience with real-time or streaming data, especially sensor or telemetry data.
• Some exposure to industrial systems: PLC, SCADA, historians, MES, CAD or maintenance systems.
• Experience preparing documents for AI, including parsing, chunking and embedding pipelines.
• A working understanding of access control and handling sensitive data.
• Containerisation, infrastructure-as-code or deployment automation.

What we offer

• Meaningful work with real impact for established engineering businesses
• A small, experienced team with a supportive and collaborative culture
• Opportunities to learn as cutting-edge problems are solved
• Flexible working that suits the role
• Competitive salary and benefits

Our interview process

1. 1Intro call30 minutes
2. 2Technical interview60 minutes
3. 3Team discussion45 minutes
4. 4OfferWe aim to move quickly

How to apply

Send us your CV and a short note about an AI system you built. Tell us how you knew it was working, and what it did for the people using it. Links to code or projects are welcome.

If you do not meet every requirement but are excited by the role and bring key strengths, we encourage you to apply. We welcome applications from people of all backgrounds, and every application is reviewed by a member of our team.