Tech Lead (Full Stack), Vice President

State Street

7d•$120,000 - $217,500•Hybrid

About The Position

The Tech Lead (Full Stack), Vice President will lead engineering delivery for a multi-year programme to modernize and migrate a large portfolio of middle-office applications to AWS. The portfolio includes cloud-native rebuilds, like-for-like migrations, and application retirements. The role owns engineering delivery and an AI-augmented delivery model that uses AI agents and assistants throughout discovery, code conversion, testing, and deployment to reduce cost and delivery time while maintaining the control and quality standards required of a global custodian. The role leads the modernization engineering team, establishes engineering standards, and owns technical delivery plans, estimates, and release quality in collaboration with Architecture, Platform, Data, QE, and application support teams. It also leads engineering cutover, parallel runs, and handover to run teams. The Tech Lead designs and operates an agentic software development lifecycle, selects and orchestrates AI tools, builds reusable agent workflows and knowledge context, and measures AI productivity and quality. The role ensures AI use is governed through human review, audit trails, secure-coding gates, expert validation of extracted business rules, reconciliation before go-live, and appropriate handling of client and confidential data. The position hires, coaches, and develops a permanent cloud-native engineering team, grows AI fluency, manages vendor and temporary capacity, and transfers knowledge to permanent staff. Success includes establishing a measurable AI-augmented delivery model, delivering modernization releases to plan without material production incidents at cutover, establishing a permanent team capable of operating and evolving the platform, and avoiding audit or control findings related to AI use.

Requirements

12+ years of experience in software engineering.
5+ years leading large engineering teams on complex modernization or cloud migration programmes.
Strong hands-on experience with Java, Spring Boot, microservices, REST, messaging such as Kafka or IBM MQ, and AWS, including ECS/EKS, IAM, and networking.
Practical experience using AI coding assistants and agents in delivery, with measurable results.
Experience migrating legacy stacks such as COBOL, C/C++, PowerBuilder, or Sybase to modern platforms, including database migration to Oracle.
Working knowledge of CI/CD and DevSecOps, such as Harness or similar, secrets management such as Vault or CyberArk, and observability.
Experience delivering in a regulated financial services environment with strong change and control requirements.
Ability to make decisions and work comfortably with ambiguity.
Credibility in code reviews and with senior stakeholders.

Nice To Haves

Knowledge of investment servicing or middle-office areas, including trade capture, confirmation, settlement, collateral, reconciliation, or investment accounting.
Experience building MCP servers, knowledge graphs, or retrieval-augmented context for engineering agents.
AWS Professional-level or equivalent certification.

Responsibilities

Lead the modernization engineering team rebuilding critical middle-office applications as cloud-native services on AWS.
Set engineering standards for microservices, event-driven integration using Kafka or MQ, containerization using ECS or EKS, and Oracle data design.
Own technical delivery plans, estimates, and release quality in collaboration with Architecture, Platform, Data, QE, and existing application support teams.
Lead the engineering aspects of cutover, parallel runs, and handover to run teams.
Design and run the programme's agentic SDLC, including AI-assisted legacy-code reverse engineering, specification generation, code conversion, test generation, and documentation.
Select, combine, and orchestrate AI tools and agent frameworks based on demonstrated effectiveness.
Build reusable agent workflows, prompt libraries, and knowledge context, including codebase knowledge graphs, vector databases, and MCP integrations.
Measure and report AI productivity and quality, including throughput, defect rates, rework, and cost per migrated application.
Establish human review checkpoints, audit trails, and secure-coding gates for AI-generated code in accordance with technology risk, information security, and model governance policies.
Ensure AI-extracted business rules are checked by subject-matter experts and confirmed through reconciliation before go-live.
Manage data classification and approved-tool usage for client and confidential data.
Hire, coach, and grow a permanent cloud-native engineering team and build AI fluency across the team and wider organization.
Manage vendor and temporary capacity and transfer knowledge to permanent staff.
Drive adoption of new ways of working while maintaining resilience and trust standards.

Benefits

401(k) retirement savings plan with company match.
Insurance coverage including basic life, medical, dental, vision, long-term disability, and optional additional coverages.
Paid time off, including vacation, sick leave, short-term disability, and leave for family care responsibilities.
Access to an Employee Assistance Program.
Eligibility for annual performance-based incentive awards, excluding certain sales roles subject to sales incentive plans.
Eligibility for certain tax-advantaged savings plans.
Inclusive development opportunities.
Flexible work-life support.
Paid volunteer days.
Employee networks.