Tech Lead MLOps

Who you are

• 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

Desirable

• 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

What the job involves

You’ll own the infrastructure behind [Employer hidden]'s production ML. You’ll lead engineers, work alongside data scientists, and improve how [Employer hidden]’s models are built, deployed, and monitored at scale

As Tech Lead, you’ll be responsible for the engineering systems that underpin Credit Risk modelling at [Employer hidden]. 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 [Employer hidden] 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 Credit Risk team builds and maintains the technology and models that determine who [Employer hidden] 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