Machine Learning Engineer Model Deployment & Inference Performance

Established financial services institution · Mid to senior level

Building a model is one thing. Making it fast, efficient and reliable in production is another. Our client is running ML at serious scale across risk, payments and customer-facing products, and they’re looking for an engineer who wants to solve the difficult bit: getting models to perform when the real-world constraints kick in.

You’ll work with data scientists and engineering teams to take models from development into production, optimise how they run, and keep them performing once they’re live.

The challenge is interesting because you can’t simply throw more compute at the problem. A significant part of the estate handles single requests, tight latency targets and fixed compute constraints.

That means profiling the bottleneck, understanding what’s really happening under the hood, making the change and proving it worked.

What you’ll be doing

• Take ML models from development into production
• Work closely with data scientists, platform and engineering teams
• Optimise inference through profiling, quantisation, graph optimisation, threading, memory and runtime tuning
• Build and improve model, data and deployment pipelines
• Benchmark performance and measure the impact of your changes
• Monitor latency, availability, model performance and drift
• Help shape reliable, scalable and secure production services
• Support retraining and the wider model lifecycle
• Work within the governance and controls expected in financial services
• Contribute to CI/CD, testing and code reviews