Senior Full Stack Engineer - Vice President

Role purpose This is a hands-on senior engineering role who sets an example for rest of the team. You'll lead engineering on a multi-year programme to modernize and migrate a large portfolio of middle-office applications to AWS. You'll write production code across the full stack (ReactJS front ends, Java back ends, DevOps pipelines). You'll also set up and run the programme's AI-augmented delivery model, using AI agents and assistants to speed up discovery, code conversion, testing and deployment while meeting the control standards a global custodian needs. Key responsibilities Hands-on full-stack engineering Design and write production code for cloud-native services and user interfaces, especially for the most complex or highest-risk components. Build ReactJS front ends that replace legacy desktop and GWT/PowerBuilder UIs, including state management, component libraries, SSO/MFA integration and accessibility. Build Java/Spring Boot microservices and APIs (REST and Kafka/MQ event-driven), with Oracle persistence on ECS/EKS. Build and maintain CI/CD pipelines (Harness or similar), infrastructure-as-code (Terraform), containerization (Docker/Kubernetes), secrets management (Vault/CyberArk) and observability. Write reference implementations, templates and starter projects that the rest of the team and AI agents follow. Do daily code reviews and pair with engineers, and debug production-grade issues yourself. Engineering leadership Lead the modernization engineering team and set standards for architecture, code quality, testing and release. Own technical delivery plans, estimates and release quality, working with Architecture, Platform, Data, QE and the existing application support teams. Lead engineering for cutover, parallel runs and handover to run teams. AI-augmented delivery Use AI tools hands-on every day (for example GitHub Copilot, Devin, Blitzy and agent frameworks), and lead from your own experience of what works. Build agent workflows, prompt libraries and codebase knowledge context (knowledge graphs, vector databases, MCP integrations). Apply AI to reverse-engineering legacy code, generating specs, converting code and generating tests. Track AI productivity and quality (throughput, defect rates, rework, cost per migrated application) and report outcomes to leadership. Risk, controls and governance Put human review checkpoints, audit trails and secure-coding gates on all AI-generated code, in line with State Street technology risk, information security and model governance policies. Make sure business logic extracted by AI is checked by subject-matter experts and confirmed through reconciliation before go-live. People and capability Hire, coach and grow a permanent full-stack engineering team, and build AI fluency across the team. Manage vendor and temporary capacity and make sure knowledge transfers to permanent staff. Required experience and skills