Chapter Lead AI & Architect

About the role

We are seeking an exceptional Software Architect and Engineering Chapter Lead to shape the technical culture and engineering excellence of our Capital Markets IT division. You will act as the guardian of software craftsmanship, ensuring every system is clean, resilient, observable, and built to last while leading a community of practice that elevates all engineers.

Key responsibilities

• Embed Clean Code principles (SOLID, DRY, YAGNI) as non‑negotiable standards across all teams.
• Own the engineering quality framework, including code review standards, static analysis gates, test‑coverage requirements and performance benchmarks.
• Lead by example: contribute to critical codebases, perform deep technical reviews and pair‑program to model best practices.
• Drive adoption of Test‑Driven Development (TDD) and Behaviour‑Driven Development (BDD) and define a robust Definition of Done.
• Design end‑to‑end, low‑latency, fault‑tolerant architectures for pricing engines, order management, risk and post‑trade platforms.
• Define and govern technology standards, reference architectures and Architectural Decision Records (ADRs).
• Champion event‑driven, domain‑driven and cloud‑native architectures.
• Integrate GenAI/Agentic AI into engineering workflows, building reusable components, prompt libraries and automated test generators.
• Lead and grow the Engineering Chapter: organize tech talks, workshops, competency frameworks and mentorship programs.

Required profile

• Proven experience as a software architect and engineering leader.
• Strong background in capital‑markets platforms or similarly complex, low‑latency systems.
• Demonstrated ability to enforce quality standards, conduct architectural reviews and mentor senior engineers.
• Experience integrating AI/GenAI tools into software development pipelines.

Required skills

• Solid understanding of SOLID, DRY, YAGNI principles.
• Expertise in TDD, BDD and test‑automation frameworks.
• Proficiency with static analysis, performance benchmarking and code‑review tooling.
• Design of event‑driven, domain‑driven and cloud‑native architectures.
• Low‑latency, fault‑tolerant system design.
• GenAI integration, prompt engineering and AI model governance.
• Architectural Decision Records (ADRs) management.