Credit Risk Platform Lead
Project description
We have been engaged by a large Australian Bank to provide an experienced engineering leader to lead their Credit Risk technology team as part of a strategic rebuild program. The program is a large initiative to re-engineer a legacy Credit Risk system that underpins the bank's credit decisioning and review processes. The team is distributed across Sydney and India and is responsible for designing and delivering the next-generation platform from the ground up. This is a greenfield build opportunity with a mandate to leverage modern engineering practices, AI-assisted development, and agentic tooling to accelerate delivery and raise the engineering bar across the team. The ideal candidate is a hands-on Principal Engineer who can set technical direction, lead distributed teams, and bring practical experience in applying AI and agent-based approaches to transform how engineering teams build software. What Makes This Role Unique • Greenfield -- Building its replacement from scratch with modern tools and approaches • AI-first mandate -- The bank is actively investing in AI-assisted engineering. You will have executive support to experiment with and embed agentic development practices across the team • Scale and impact -- Leading large engineering team on a program that directly impacts the bank's credit risk posture and regulatory standing • Domain depth -- Credit Risk decisioning is a complex, high-stakes domain where strong engineering can create outsized business value
Responsibilities
- Define and own the technical architecture and engineering roadmap for the greenfield Credit Risk platform, replacing the legacy decisioning and review system
- Lead, coach and grow high performance and continuous improvement team including software engineers, data engineers and QA.
- Collaborate closely with Credit Risk business stakeholders, Product Owners, and Risk leadership to translate business requirements into engineering deliverables
- Partner with the bank's broader technology leadership to align the Credit Risk platform with enterprise architecture, security, and compliance standards
- Drive engineering best practices include CI/CD, infrastructure-as-code, automated testing, performance monitoring and automated health checks.
- Introduce and embed AI-assisted development practices across the team, including the use of coding agents, AI pair-programming tools, and automated code generation
- Design and build AI agent-based solutions where applicable within the Credit Risk domain (e.g., automated credit decisioning workflows, intelligent document review, risk assessment pipelines)
- Contribute hands-on to critical design decisions and complex engineering work.
SKILLS
Must have
- 12+ years of software engineering experience with at least 3 years in a Principal Engineer, or Engineering Manager capacity
- Demonstrated experience leading and growing engineering teams of 15+ people, in distributed / multi-geography setups
- Proven track record of delivering large complex greenfield implementations or builds large-scale system or delivering multiyear cross platform simplification programs in financial services
- Strong domain knowledge in Credit or Market Risk or Good capital markets knowledge with experience in Pricing / Quants valuation.
- Familiarity with credit risk modelling concepts PD, LGD, EAD, credit scorecards, and decisioning engines
- Hands-on experience building, deploying, or integrating AI agents and LLM-based tooling into engineering workflows using tools like Claude Code / Cursor / Ampcode (e.g., coding assistants, autonomous agents, RAG pipelines, and agentic workflows)
- Demonstrable success in lifting engineering team capability through AI tools -- quantifiable improvements in velocity, quality, or developer experience
- Deep proficiency in modern backend technologies (e.g., Java, Kotlin, Python, or similar) and cloud-native architectures (AWS, Azure, or GCP)
- Strong understanding of API design, event-driven architecture, microservices, and domain-driven design
- Experience with modern CI/CD pipelines, automated testing strategies, and DevOps practices
- Excellent communication and stakeholder management skills, with the ability to translate between technical and business language
- Experience working in regulated banking or financial services environments with an understanding of risk and compliance constraints
Nice to have
• Experience with specific Trading / Credit Risk platforms or vendors (e.g., Murex, Calypso, Moody's, internal bank-built systems) • Experience with data engineering and analytics platforms (e.g., Spark, Databricks, Snowflake) • Experience with front-end technologies for building internal risk management dashboards and tooling • Contributions to open-source projects or published thought leadership on AI-assisted engineering
