Engineering Full-Time

AI Engineer, London

Distyl • London, UK

Company

Distyl

Annual salary

Not disclosed

Location

London, UK

Listed

2d ago

Experience:: 5 to 15 years
Workplace:: 3 days in-office (Tue, Thu) in London, UK office
Equity:: Competitive equity

Hybrid in London, UK.

Send us your LinkedIn and CV. If your experience fits, we'll introduce you to the recruiter filling this role.

Apply for a referral →

The recruiter emails you before anything happens and may suggest other jobs that suit you better.

What you'll do

**Build Production AI Systems: **Design, develop, and deploy robust AI applications using LLMs, including prompt engineering, agent workflows, tool use, and full-stack AI products
**Work Directly with Customers: **Partner closely with enterprise stakeholders to understand complex problems and translate them into impactful AI solutions
**Lead System Architecture: **Design scalable architectures for production AI systems, balancing performance, reliability, cost, and maintainability
**Develop Our Internal Platform: **Contribute to Distillery, our internal LLM application platform, by building reusable infrastructure, tools, and workflows used across customer deployments
**Evaluate AI Systems Rigorously: **Develop evaluation frameworks that measure model performance across accuracy, latency, cost, reliability, and safety
**Ship Production-Grade Systems: **Ensure systems meet high standards for observability, reliability, security, and maintainability
**Raise the Engineering Bar: **Improve development workflows, evaluation practices, and deployment strategies as our AI platform continues to evolve

What you need

We’re opening an office in London, UK and looking for AI Engineersto design and deploy production-grade AI systems powered by LLMs.
At Distyl, AI Engineers work directly with Fortune 500 companies to transform complex workflows using AI. You’ll build and ship real-world AI applications, from intelligent agents to full-stack AI products, and see them operate at scale in mission-critical environments.
This role is highly hands-on. You’ll collaborate closely with customers, define system architectures, and build reliable, high-impact AI systems from prototype to production. Engineers at Distyl also help shape technical direction across major customer engagements, guiding enterprise teams through AI adoption and deployment.

Frequently Asked Questions

How do I apply for the AI Engineer, London role at Distyl? +

Use the Apply for a referral button on this page to send us your LinkedIn and CV. If your experience fits, we'll introduce you to the recruiter filling this role. They'll email you to check you're interested, then put you forward for this job or others that suit you better. It's free.

Is this role remote? +

The listing gives the location as London, UK. 3 days in-office (Tue, Thu) in London, UK office.

Interview Prep

Sample questions for a AI Engineer role, written in-house to help you prepare.

How do you decide which evaluation metric to optimize for a classification model when the classes are imbalanced?

Accuracy is misleading on imbalanced data because a model can score well by always predicting the majority class. I look at precision, recall, and F1 for the minority class specifically, and pick the metric that matches the real cost of false positives versus false negatives in the deployment context, since those costs are rarely symmetric in practice.

What is your process for debugging a deep learning model that trains fine but performs poorly at inference time?

I start by checking for a train/inference mismatch: different preprocessing, batch normalization behaving differently in eval mode, or data leakage during training that inflated validation scores. Comparing the exact input pipeline used at training time against the one used at inference usually surfaces the discrepancy before I need to touch the model architecture itself.

How do you approach feature selection when working with high-dimensional tabular data?

I start with correlation and mutual information analysis to drop obviously redundant features, then use a tree-based model's feature importance as a first pass filter. From there, recursive feature elimination with cross-validation tells me where the marginal value of additional features drops off, which keeps the final feature set both smaller and more robust to overfitting.

Explain how you would detect and address data drift in a production machine learning pipeline.

I monitor the statistical distribution of input features and model outputs over time, using something like population stability index or KL divergence against a reference window. When drift crosses a threshold, I retrain on recent data rather than the full historical set, and I keep an alert in place so drift is caught before it shows up as a quality regression.

See all 10 questions for this role →