Software Engineering Vice President - Fiduciary Oversight Model Eng

The Core Engineering 

The Core Engineering builds and operates the platforms, applications, data solutions, models, and analytics that power critical processes for The Core divisions of the firm (e.g., Risk, responsible for the risk profile of firm activities; Controllers, responsible for the financial control and reporting obligations; Compliance, responsible for the firm’s compliance, regulatory, and reputational risks; Corporate Treasury, responsible for the firm’s liquidity, funding, balance sheet, etc.; and Human Capital Management, responsible for attracting, developing, and managing a global workforce). A centralized engineering structure in support of The Core enables a common platform model and operating framework that promotes consistent governance and scalable solutions, leveraging cloud, AI, and machine learning for innovation and efficiency. The Core Engineering’s 2,000+ engineers and strats deliver engineering, data, analytics, and quantitative capabilities within six business units: 

• Metrics & Analytics Platforms: responsible for the measurement and management of the firm’s risk, capital, and liquidity for The Core functions

• The Core Strats: responsible for the development and implementation of models and other quantitative methodologies, including the accuracy and attribution of modeled metrics

• Financials & Reporting: responsible for facilitating the production of the firm’s financials and a wide range of reporting functions

• Non-Financial Risk & Controls: responsible for non-financial risk and control processes

• Enterprise Platforms: responsible for platforms and applications that support critical operational processes across The Core such as payments, people processes, and procurement

• Shared Services: responsible for driving the adoption of consistent engineering strategy, including data platforms, cloud, and AI enablement, as well as the management of technology risk

HOW YOU WILL FULFILL YOUR POTENTIAL

As a member of our team, you will:

• partner globally with sponsors, users and engineering colleagues across multiple divisions to create end-to-end solutions,
• learn from experts
• leverage various technologies depending on the team including Java, Python, PySpark, SQL, Hadoop, Snowflake, Iceberg, Kafka, Kubernetes
• apply analytical and functional skills to design surveillance processes to identify risks
• be able to innovate and incubate new ideas
• have an opportunity to work on a broad range of problems, often dealing with large data sets, including real-time processing, messaging, workflow and UI/UX
• be involved in the full life cycle; defining, designing, implementing, testing, deploying, and maintaining software across our products.

AI‑Assisted Engineering & Productivity

In addition, you will:

• Effectively use AI‑assisted software development tools (e.g., GitHub Copilot, Devin, Claude Code, or equivalent) to improve developer productivity and reduce development cycle time.
• Apply AI tools to accelerate: 
  • code generation and refactoring,
  • test creation and coverage improvement,
  • debugging, root‑cause analysis, and performance optimization.
• Use AI‑assisted reasoning to understand complex codebases, rapidly prototype solutions, and improve code quality while maintaining strong engineering standards.
• Partner with peers and reviewers to validate, harden, and productionize AI‑generated outputs, ensuring correctness, security, maintainability, and regulatory compliance.
• Identify opportunities where AI tooling can reduce manual effort, minimize rework, and support faster, higher‑quality delivery to production.

QUALIFICATIONS

A successful candidate will possess the following attributes:

1. A Bachelor's or Master's degree in Computer Science, Computer Engineering, or a similar field of study.
2. 5+ years professional software development experience
3. Expertise in Java and/or Python development. 
4. Experience in automated testing and SDLC concepts.
5. The ability (and tenacity) to clearly express ideas and arguments in meetings and on paper.

Experience in some of the following is desired and can set you apart from other candidates: 

• Demonstrated experience or a strong interest in leveraging AI-assisted developer tools to enhance engineering productivity and accelerate software delivery.
• Ability to critically evaluate AI‑generated outputs and enhance them to production‑grade quality. 
• Proven ability to critically evaluate AI-generated outputs and refine them to meet production-grade standards of quality, reliability, and maintainability.
• Hands-on proficiency with AI coding assistants such as GitHub Copilot, Claude Code, and Devin to expedite code authoring, refactoring, and debugging across all phases of the Software Development Life Cycle (SDLC).
• Effective utilization of AI pair-programming tools to generate boilerplate code, unit tests, and technical documentation, enabling greater focus on architectural design and complex problem-solving.
• Application of established prompt engineering best practices to elicit high-quality, context-aware code recommendations and reduce iteration cycles during development.
• Practical integration of autonomous AI agents (e.g., Devin) for routine engineering tasks, including bug triage, dependency upgrades, and minor feature implementations, thereby freeing engineering bandwidth for higher-impact initiatives.
• Adoption of AI-driven code review tools to proactively identify defects, security vulnerabilities, and performance bottlenecks earlier in the development cycle, strengthening overall quality gates.
• Experience with: 
  • API design and micro-services architecture, such as to create interconnected services
  • Cloud-based data platforms, such as Snowflake
  • Apache Spark and/or Hadoop
  • Data Lake or Lakehouse solutions
  • Relational databases
  • Knowledge of the financial industry and compliance or risk functions
  • Influencing and collaborating with stakeholders.