4815 - Full Stack AI Engineer, Global Information Security (Vice President)

Company

Jefferies, the global investment banking firm, has served companies and investors for almost 60 years. Headquartered in New York with its European head office in London, the firm provides clients with capital markets and financial advisory services, institutional brokerage and securities research, and asset management. Jefferies provides research and execution services in equity, fixed income, foreign exchange, and a full range of investment banking services including underwriting, merger & acquisition, restructuring and recapitalisation and other advisory services, with businesses operating in the Americas, Europe and Asia.

Overview

Global Information Security is building and operating software that helps security teams work more effectively. We are looking for a hands-on full-stack engineer to turn operational needs into reliable, secure products used by analysts and engineers across multiple regions.

The core of this role is software development: designing and delivering useful user interfaces, back-end services, APIs and data flows, then supporting those systems in production. You will collaborate with teams across Security Operations, identity and access management, privileged access management, cloud security, network security, data protection and vulnerability management.

Automation and AI may be part of a solution, but they are not the starting point for every problem. We value strong engineering fundamentals, sound technical judgment and the ability to build and maintain software, not simply generate code or assemble prototypes. You should be comfortable using development tools, including AI coding assistants, while remaining able to understand, test, debug and take responsibility for the code you deliver.

Responsibilities

• Design, build and maintain full-stack applications, including user-facing interfaces, back-end services, APIs and data integrations.
• Maintain and extend agentic AI workflows — agent nodes, prompts, guardrails, tool bindings, and human-in-the-loop approval gates — including evaluation and bias-governance of AI scoring.
• Manage data models and pipelines in MongoDB/DocumentDB, ensuring data quality, sync integrity across systems of record, and auditability.
• Own the AWS infrastructure via IaC and CI/CD; manage environments, releases, security posture (SSO/RBAC), and cost.
• Maintain observability: dashboards, agent audit trails, structured logs, alerts, and operational runbooks.
• Work with security teams to understand operational challenges, shape requirements and deliver practical software that analysts can adopt in their daily work.
• Write readable, maintainable code and contribute to sound application architecture, automated testing, code review and technical documentation.
• Build and maintain REST APIs and integrations with platforms such as SIEM, EDR, SOAR, PAM, IGA, CSPM, DLP, ticketing and threat intelligence systems.
• Create and maintain CI/CD pipelines that automate build, test, security checks and deployment.
• Package and run services using Docker; contribute to deployments in Kubernetes-based environments with an understanding of core concepts and workflows.
• Operate production software by contributing to monitoring, alerting, incident investigation, reliability improvements and lifecycle management.
• Apply secure development practices, including input validation, secrets management, least-privilege access, audit logging and appropriate approval controls.
• Identify where workflow automation or AI can improve a process, and where a straightforward, deterministic software solution is more appropriate.
• Where suitable, develop AI-enabled features and workflow integrations, including LLM APIs, tool calling or MCP integrations, with appropriate evaluation and human oversight.
• Measure the impact of delivered software through adoption, time saved, reduced cycle times, accuracy and service quality.
• Collaborate with colleagues and stakeholders across time zones; share knowledge and contribute to engineering standards and documentation.
• Mentor engineers and support consistent delivery practices across the global team.

Required qualifications

• At least 5 years of professional software engineering experience, including responsibility for software running in production.
• Demonstrated full-stack development experience, delivering both user-facing applications and back-end services.
• Strong proficiency in at least one modern front-end language and framework, such as TypeScript/JavaScript with React or an equivalent, and a back-end language such as Python, Java, C# or Node.js.
• Experience designing and integrating REST APIs, handling authentication, and working with relational or document-oriented databases.
• Solid engineering fundamentals, including automated testing, debugging, code review, version control and maintainable software design.
• Practical experience creating or maintaining CI/CD pipelines for automated build, test and deployment.
• Good working knowledge of Docker, including building and troubleshooting container images and running containerised applications.
• Basic familiarity with Kubernetes concepts, such as pods, deployments, services, configuration and logs.
• Understanding of Terraform and infrastructure-as-code concepts.
• Secure development experience, including secrets handling, access control, input validation and auditability.
• Ability to take a loosely defined problem from discovery through implementation, release and production support.
• Experience working with distributed teams and stakeholders across multiple time zones.

Preferred qualifications

• Experience in financial services or another regulated industry, including familiarity with audit, evidence and change-control requirements.
• Experience building software and agentic workflows for security operations or another security domain, such as IAM, PAM, cloud security, network security, data protection or vulnerability management.
• Experience integrating enterprise platforms using webhooks, event-driven patterns or workflow automation tools.
• Practical experience using LLM APIs, agent runtimes or MCP integrations in software products.
• Familiarity with AI-related risks such as prompt injection, unsafe tool use and excessive autonomy, and ways to mitigate them.
• Experience with production observability, including structured logging, metrics, tracing and alerting.
• Experience using code-generation tools or AI coding assistants responsibly, with the ability to review, test and explain the resulting code.
• Experience transitioning vendor-managed software or automation to internally owned services.
• Open-source contributions, published research or conference speaking.

Candidate Profile

The successful candidate is a software engineer first: someone who can understand a problem, choose an appropriate design and deliver a complete, supportable solution. They are comfortable moving between front-end and back-end work, building integrations, improving pipelines and diagnosing production issues.

They take ownership of their work and can explain the code they write, including code developed with assistance from automation or AI tools. They value testing, review and maintainability, and know that a working demo is only the beginning of a production service.

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