Senior Software Engineer - ML & AI (AI Products)

Description

The role is a software engineering position, not a modelling one: you will build and run the services, pipelines and infrastructure that deliver LLM-driven and agentic features to millions of small businesses each day, rather than training or tuning models.

Your architecture choices will shape scale, cost, reliability and how quickly teams can ship.

No machine learning background is required. The company wants strong production engineers, ideally with LLM or agentic system experience, and ML knowledge can be picked up within the team.

You'll join AI Products, a globally distributed group of scientists, engineers, product managers and analysts inside the wider Data & AI business.

Engineers work with Applied Scientists from the start to build the interfaces and harnesses that move models into production. After launch they own the engineering and infrastructure of each ML and AI system, covering reliability, cost and scale.

Current work includes production ML and AI platforms and services, a conversational GenAI assistant that is becoming more context-aware and agentic, automated bank reconciliation and smart document capture that use ML and AI to match transactions and extract data from bills and receipts, a platform that lets agents plan and run multi-step tasks across the company's products, and AI features in customer-facing flows along with their backend and orchestration.

Responsibilities

• Design, build and run the services, pipelines and infrastructure behind LLM-driven and agentic features.
• Make architecture decisions that set scale, cost and reliability.
• Set engineering standards and mentor other engineers to lift capability across AI Products.
• Work with teams across the company to make data usable and keep systems dependable.

Requirements

• Have shipped and operated production backend systems at scale.
• Have built and run LLM or agentic workloads, including orchestration, tool use, evaluation, guardrails, and control of latency and cost.
• Strong system design skills and solid coding in Python, or another web-scale language such as Go, Java, Kotlin or TypeScript.
• Comfortable designing APIs and services and working with cloud infrastructure (ideally AWS), CI/CD and observability, with attention to latency, cost, reliability and SLOs.
• Have run services in production, including on-call duty and incident response.
• Solid SQL and experience with large-scale data processing.
• Communicate clearly, explaining complex technical ideas to both business and technical audiences.
• Coach others and set engineering standards.
• Have operationalised ML or LLM workloads, such as inference serving and evaluation pipelines.
• Familiar with MLflow, PyTorch or TensorFlow, and data orchestration tools.
• Experience with distributed processing, such as Spark or Dask.

Conditions

Hybrid work: you split time between modern offices and remote work, with set office days and shared 'boost days' to collaborate and ship.

Candidates are encouraged to apply even if they don't meet every requirement, since hiring considers skills, motivation and the perspective each person brings to the team and culture.