Software Engineering Lead / Applied AI Engineering

You will lead a multidisciplinary engineering team delivering AI-powered services and tools for digital identity, fraud detection, and behavioural intelligence. The role spans technical leadership, delivery management, hands-on architecture, and coaching of engineers in a rapidly evolving AI platform environment.

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.

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.

Preferred Qualifications

• Experience building fraud, identity, risk, or security systems.
• Experience running teams using AI and LLM development tooling and automation.
• Knowledge of feature stores, model monitoring, or real-time scoring systems.