AI Chapter Lead

Main Responsibilities

Software Engineering & Craftsmanship

• Embed Clean Code principles (e.g. SOLID, DRY, YAGNI) as non-negotiable standards across all teams.
• Own the engineering quality framework: code review standards, static analysis gates, test coverage requirements, and performance benchmarks.
• Lead by example — contribute to critical codebases, perform deep technical code reviews, and pair-program with engineers to model best practice.
• Drive Test-Driven Development (TDD) and Behaviour-Driven Development (BDD) adoption to improve correctness and documentation.
• Define and enforce Definition of Done (DoD) criteria that include architectural, security, and performance quality gates.

Software Architecture

• Design end-to-end architectures for capital markets platforms (pricing engines, order management, risk, post-trade) prioritizing low latency, fault-tolerance, and auditability.
• Define and govern technology standards, patterns, and reference architectures across the division.
• Drive architectural reviews and Architectural Decision Records (ADRs), ensuring decisions are documented, peer-reviewed, and communicated.
• Balance tactical delivery needs with long-term architectural health — managing trade-offs transparently.
• Champion event-driven, domain-driven, and cloud-native architectures where appropriate.

GenAI / Agentic AI for Software Engineering

• Integrate GenAI/Agentic AI into engineering workflows to accelerate coding, testing, documentation, and troubleshooting.
• Build reusable GenAI, MCP components, prompt libraries, engineering agents, automated test generators and code refactoring assistants.
• Partner with enterprise AI teams to ensure governance, data safeguards, and safe model usage.
• Track and report measurable productivity, quality, and cycle-time improvements enabled by GenAI.

Chapter Leadership

• Lead and grow the Engineering Chapter: a cross-filiere community of practice for software engineers across Capital Markets IT.
• Convene regular Chapter sessions, tech talks, architecture story boards, refactoring workshops, and book clubs to foster continuous learning.
• Define and maintain a shared engineering competency framework and career ladder for software engineers.
• Mentor senior and principal engineers; provide structured technical coaching aligned with individual growth plans.
• Collaborate with Tribe Leads and Product Owners to ensure engineering quality does not erode under delivery pressure.

Technical Governance & Strategy

• Own the technology radar for Capital Markets IT: assess, trial, adopt or hold technologies in partnership with the global architecture and engineering teams.
• Lead the technical due diligence on vendor solutions, open-source frameworks, and cloud services.
• Track and present engineering health metrics (code quality, deployment frequency, MTTR, change failure rate) to leadership.
• Partner with Cloud, Security and infrastructure teams to embed shift-left practices into the SDLC.

Qualifications and Profile

• Master or Bachelor’s degree in Computer Science/ Information Technology/ Programming & Systems Analysis/ Science (Computer Studies) faculties.
• AI Proficiency
• Demonstrated ability to effectively utilize AI-powered tools (e.g., GitHub Copilot) to enhance productivity and problem-solving capabilities.
• Understanding of AI/ML fundamentals including prompt engineering, model limitations, and best practices for human-AI collaboration.
• Experience in evaluating AI-generated outputs for accuracy, security, and alignment with business requirements.
• Ability to identify opportunities for AI integration and automation within existing workflows and processes.

Domain & Technical Background

• 10+ years of software engineering experience, with at least 4 years in a principal, staff, or architect role.
• Core: Deep hands-on expertise in at least one primary language ecosystem.
• Java / Kotlin (Spring Boot, Project Loom, GraalVM).
• Python (asyncio, Cython, NumPy/Pandas for quant workflows).
• C++ (modern C++20, lock-free structures, FPGA/kernel bypass desirable).
• Proven background in Capital Markets IT: trading systems, risk engines, order routing, or post-trade processing.
• Experience designing distributed systems with strong consistency, exactly-once semantics, and sub-millisecond latency requirements.
• Hands-on experience with messaging infrastructure: Kafka, Solace, or similar low-latency brokers.
• Cloud architecture experience (AWS, Azure, or GCP) with an understanding of hybrid cloud patterns common in regulated financial environments.
• Practical experience applying GenAI & Agentic AI tools/frameworks in enterprise engineering workflows.

Craftsmanship & Engineering Excellence

• Demonstrable commitment to Clean Code and software craftsmanship; able to articulate and teach these principles.
• Experience implementing and governing CI/CD pipelines with quality gates (SonarQube, Checkmarx, Veracode, or equivalent).
• Strong understanding of software testing strategies: unit, integration, contract, performance, and chaos engineering.
• Familiarity with Domain-Driven Design (DDD), Event Sourcing, and CQRS patterns in a financial domain context.