AI/ML Engineering Lead(Capital Market domain exp. is Mandatory)

Project Description:

Our client is the corporate and investment banking arm of The Group, world's 12th largest bank by total assets.

We are looking for an exceptional Software Architect and Engineering Chapter Lead to shape the technical culture and engineering excellence of our client's Capital Markets IT division. This position will be the guardian of software craftsmanship ensuring that every system we build is clean, resilient, observable, and built to last. Beyond your individual architectural contributions, you will animate our Engineering Chapter: a community of practice that elevates every engineer on the floor through mentorship, standards, and relentless pursuit of quality

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) prioritising 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.

Mandatory Skills Description:

• 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:

o Java / Kotlin (Spring Boot, Project Loom, GraalVM)

o Python (asyncio, Cython, NumPy/Pandas for quant workflows)

o 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

Leadership & Chapter / Community of Practice

• Experience running or participating in a Chapter, Guild, or Centre of Excellence within an Agile/SAFe/Spotify model organisation

• Demonstrable ability to define and operationalise engineering standards at scale — across multiple squads or delivery teams

• Strong technical communication: able to translate complex architectural decisions for both engineering and business audiences

• Track record of developing senior engineering talent through mentoring, structured feedback, and technical coaching