Job Description
Join us to build market-leading technology products and grow your skills while working with a collaborative, high-performing engineering team.
Job summary
As a Lead Software Engineer within Corporate Sector in Governance and Controls Technology (GCT), you will design and deliver trusted, market-leading products in a secure, stable, and scalable way. You will work across multiple technical areas and business functions, partnering with your team to deliver solutions that matter to our customers and the firm. You help strengthen our engineering culture through ownership, continuous improvement, and inclusive collaboration.
Job responsibilities
- Execute software solutions through design, development, and technical troubleshooting, thinking beyond routine approaches to solve complex problems
- Create secure, high-quality production code and maintain algorithms that run synchronously with appropriate systems
- Produce architecture and design artifacts for complex applications and ensure design constraints are met through software code development
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets to drive continuous improvement of applications and systems
- Proactively identify hidden problems and patterns in data and use insights to improve coding hygiene and system architecture
- Contribute to software engineering communities of practice and events that explore new and emerging technologies
- Ability to independently work on modules with minimal support
Required qualifications, capabilities, and skills
- Practical experience in system design, application development, testing, and operational stability
- Hands-on experience in full stack software engineering, with a track record of developing and deploying business critical applications in production.
- Proficiency in Java, Spring Boot, JPA, SQL with the ability to learn additional programming languages as needed
- Experience with React and other front end technologies
- Experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of incorporating AI use in engineering workflows, including data sensitivity considerations, curation of inputs, measuring performance, model fine tuning and adherence to resiliency and security expectations
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Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
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Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Knowledge of cloud-based development
- Experience developing, debugging, and maintaining code in a large corporate environment, including use of database querying languages
- Overall knowledge of the Software Development Life Cycle, including agile practices such as CI/CD, application resiliency, and security
Preferred qualifications, capabilities, and skills
- Experience in programming languages like Python for model development, experimentation, and integration with LLM API’s.
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.