Tech Lead - MLOps
Hybrid in London, United Kingdom
We’re looking for a Tech Lead – MLOPs
You’ll own the infrastructure behind iwoca's production ML. You’ll lead engineers, work alongside data scientists, and improve how iwoca’s models are built, deployed, and monitored at scale.
The company
Small businesses move fast. Opportunities often don’t wait, and cash flow pressures can appear overnight. To keep going, and growing, SMEs need finance that’s as flexible and responsive as they are.
That's why we built iwoca. Our smart technology, data science and five-star customer service ensures business owners can act with the speed, confidence and control they need, exactly when it's needed.
We’ve already cleared the way for 100,000 businesses with more than £4 billion in funding. Our passionate team is driven to help even more SMEs succeed, through access to better finance and other services that make running a business easier. Our ultimate mission is to support one million SMEs in their defining moments, creating lasting impact for the communities and economies they drive.
The team
The Credit Risk team builds and maintains the technology and models that determine who iwoca lends to, how much, and on what terms. Their work covers credit scoring, scorecard development, approval thresholds, and portfolio monitoring – all working to maximise lending volume without taking on disproportionate risk.
The team has nine data scientists, four engineers, and two strategy analysts. The data scientists analyse past data and make the models; the engineers build and maintain the supporting infrastructure; and the strategy analysts translate model outputs into lending decisions.
The role
As Tech Lead, you’ll be responsible for the engineering systems that underpin Credit Risk modelling at iwoca. You’ll focus on the platform, pipelines, and production systems around the models, rather than building models yourself. This is a hands-on technical leadership role, combining technical direction, line management, and individual contribution.
Technical leadership
- Lead four engineers working on the modelling platform and surrounding systems; help the team deliver high-quality solutions.
- Raise engineering standards across modelling systems and infrastructure, with a focus on reliability, observability, reproducibility, and safe change.
- Set technical direction by understanding adjacent systems, workflows, and constraints to identify the right problems to solve at the right time.
Individual contribution
- Stay hands-on in the codebase, especially when the team is dealing with ambiguity, cross-system complexity, or problems without an obvious owner.
- Lead by example in how problems are approached and how rigour is balanced with speed of delivery.
- Contribute directly to the systems that matter most, making pragmatic changes that reduce friction, strengthen the platform, and improve reliability in production.
Work that matters
- Engineering decisions in this area directly affect the accuracy of our credit decisions and the volume of lending iwoca can responsibly support.
- Better infrastructure leads to faster experimentation and better models
- Help the team to work faster and with more confidence in the platform. Work with data scientists and strategy analysts to maintain the feedback loop between analysis, strategy and engineering.
The salary
We expect to pay from £100,000 - £150,000 for this role. But, we’re open-minded, so definitely include your salary goals with your application. We routinely benchmark salaries against market rates, and run quarterly performance and salary reviews.
The culture
At iwoca, we prioritise a culture of learning, growth, and support, and invest in the development of our team members. We value thought and skill diversity, and encourage people to explore new areas of interest, adopt better tools — including AI — and apply sound judgement so our products and decisions improve over time.
The offices
We put a lot of effort into making iwoca a great place to work:
- Offices in London, Leeds, Berlin, and Frankfurt with plenty of drinks and snacks.
- Events and community-led groups, including running groups, padel, and monthly ping-pong and pool competitions.
The benefits
- Flexible working hours.
- Medical insurance from Vitality, including discounted gym membership.
- A private GP service (separate from Vitality) for you, your partner, and your dependents.
- 25 days’ holiday per year, an extra day off for your birthday, the option to buy or sell an additional five days of annual leave, and unlimited unpaid leave.
- A one-month, fully paid sabbatical after four years.
- Instant access to external counselling and therapy sessions for team members that need emotional or mental health support.
- 3% Pension contributions on total earnings.
- An employee equity incentive scheme.
- Generous parental leave and a nursery tax benefit scheme to help you save money.
- Electric car scheme and cycle to work scheme.
- Two company retreats a year: we’ve been to France, Italy, Spain, and further afield.
And to make sure we all keep learning, we offer:
- A learning and development budget for everyone.
- Company-wide talks with internal and external speakers.
- Access to learning platforms like Treehouse.
Useful links:
The requirements
Essential:
- A background in a highly numerical discipline – mathematics, physics, statistics, or similar – with an interest in ML and modelling, whether from professional experience or independent work.
- A generalist mindset, with the flexibility and confidence to understand broad technical contexts, challenge assumptions, and suggest a better approach.
- Experience leading engineering teams, combining technical leadership, line management, and individual contribution to deliver cross-functional projects.
- Confidence working in a team where the primary output is statistical models – though you'll own the engineering that supports that work, not the models themselves.
- Advanced proficiency in Python or another object-oriented language.
- A commitment to using modern tools effectively – including AI – to maximise quality, speed, and rigour, while retaining responsibility for accuracy and outcomes.
Bonus:
- Experience designing and operating ML systems in production, including model serving, monitoring, and retraining pipelines.
- Experience with ML-specific engineering constraints, including data immutability, temporal consistency, and feature store design.
- Experience with experiment tracking platforms, model registries, or MLflow-style tooling.
- Familiarity with Snowflake, PostgreSQL, or similar data platforms used in ML workflows.
- Exposure to probabilistic modelling, Bayesian methods, or statistical inference in a production context.
- Experience with event-sourced data models or time series data pipelines.
