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

Other

Languages

English: C2 Proficient

Seniority

Senior

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Req. VR-121689

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