Software Engineering Lead / Applied AI Engineering

Overview

As Lead Engineer, you will guide a multidisciplinary team delivering AI-powered services for digital identity, fraud detection, and behavioural intelligence. You’ll shape technical direction, drive ML deployment pipelines, and ensure high-quality delivery with strong observability. You’ll partner with Product, Architecture and ML Research to prioritise work and promote modern AI practices. This is a hands-on leadership role in a fast-evolving AI platform environment, focused on scalable, secure systems. You’ll have the chance to impact risk decisions and operational efficiency at scale.

Pay / Benefits

• Health screening and private medical benefits
• Pension scheme
• Share option scheme
• Travel season ticket loan
• Electric Vehicle Scheme
• Maternity, paternity and shared parental leave

Responsibilities

• Lead and grow a team of full-stack ML engineers, QA engineers, and a UI developer
• Define technical direction for AI-enhanced services, internal tools, and platform components
• Drive architecture for model deployment pipelines, inference APIs, and data and feature systems
• Ensure high-quality delivery across code quality, testing, documentation, and observability
• Partner with Product, Architecture, and ML Research teams to prioritise and scope work
• Foster a culture of modern AI development practices — LLM tooling, MLOps, automation
• Set and enforce DevOps and SecOps standards across the team’s services and pipelines
• Coordinate cross-team dependencies and contribute to roadmap planning
• Support hiring, onboarding, and performance development within the team

Key requirements

• 7+ years in backend, full-stack, ML engineering, or distributed systems
• 2+ years in technical leadership, team leadership, or senior mentoring roles
• Hands-on experience deploying ML-powered services into production
• Strong Python and Java — both are in active use across the team’s production services
• Experience with Snowflake/Spark/Databricks or others, CI/CD pipelines, and modern DevOps tooling
• Solid understanding of SecOps practices and security-conscious system design
• Demonstrable track record of taking initiative and driving work independently
• Working knowledge of DevOps and SecOps practices deployment patterns, and security-aware engineering
• Broad full-stack curiosity: comfortable picking up work outside your primary discipline when the problem demands it

• leadership
• cross-functional collaboration
• initiative
• Python
• Java
• ML deployment in production

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