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Apps Dev Tech Lead Analyst

Job in Jersey City, Hudson County, New Jersey, 07390, USA
Listing for: Citigroup Inc.
Full Time position
Listed on 2026-05-23
Job specializations:
  • Software Development
    AI Engineer, Cloud Engineer - Software, Software Engineer, DevOps
Salary/Wage Range or Industry Benchmark: 130000 - 160000 USD Yearly USD 130000.00 160000.00 YEAR
Job Description & How to Apply Below

Apps Development Senior Manager

Trade Manager Zone Platform | Cash Securities Settlements | Equity Growth Initiative About the Role

We are seeking a visionary and hands‑on Apps Development Senior Manager to lead engineering delivery for the Trade Manager Zone (TMZ) platform within the Cash Securities Settlements group, supporting the Equity Growth Initiative
. This is a core engineering leadership role at the heart of a high‑impact, high‑throughput financial platform that processes equity trade settlements at scale.

You will lead high‑performing engineering teams responsible for building, maintaining, and evolving mission‑critical settlement systems — driving architectural excellence, modern delivery practices, and technical transformation. Critically, you will play a defining role in embedding Artificial Intelligence and Machine Learning into the platform — from AI‑assisted development and intelligent automation to predictive analytics and GenAI‑powered operational tooling — accelerating delivery, elevating code quality, and future‑proofing our engineering culture.

This role demands deep technical expertise, strong ownership, and the ability to lead through complexity in a regulated, fast‑paced financial environment.

Responsibilities:
1. Software Development & System Design
  • Architect, design, develop, and maintain robust, scalable, and high‑performance applications supporting equity trade settlement workflows on the Trade Manager Zone platform.
  • Lead the design of distributed, fault‑tolerant, real‑time systems capable of handling high‑volume, low‑latency trade processing across global markets.
  • Champion the use of AI‑assisted coding tools (e.g., Git Hub Copilot) to accelerate developer productivity, reduce toil, and improve code quality.
  • Drive adoption of trunk‑based development practices to enable continuous integration and rapid, safe delivery.
  • Ensure code is clean, maintainable, and testable — adhering to SOLID principles
    , design patterns, and platform engineering standards.
  • Actively contribute to hands‑on coding, code reviews, and refactoring to maintain high engineering standards across the team.
  • Own the technical design of key platform components, producing clear architecture documentation and decision records.
2. Engineering Excellence & AI‑Driven Quality
  • Champion Test‑Driven Development (TDD),
    Behavior‑Driven Development (BDD), and high unit test coverage as non‑negotiable engineering standards.
  • Introduce AI‑powered code review tooling to complement human reviews — catching security vulnerabilities, anti‑patterns, and performance issues at scale.
  • Apply predictive quality analytics to identify high‑risk code changes before they reach production.
  • Drive the adoption of automated testing frameworks across unit, integration, regression, and end‑to‑end test layers, including AI‑assisted test generation.
  • Implement and enforce secure coding practices
    , augmented by AI‑driven vulnerability scanning, performing assessments and ensuring compliance with financial industry security and regulatory standards.
  • Advocate for infrastructure as code
    , continuous monitoring, and AI‑assisted observability (e.g., anomaly detection, intelligent alerting, root cause analysis) to enhance platform reliability.
  • Embed CI/CD pipelines and Dev Ops practices deeply into the team’s delivery workflow, aligned to Citi Engineering Excellence Standards
    .
  • Drive a culture of zero‑defect engineering — proactive quality ownership from design through to production.
3. Advanced AI & Data Integration
  • Lead the integration of AI and ML capabilities into the Trade Manager Zone platform — including intelligent exception handling, anomaly detection, predictive settlement failure analysis, and automated reconciliation workflows.
  • Leverage Large Language Models (LLMs) and GenAI APIs to embed intelligent capabilities into platform operations — such as natural language interfaces for trade operations, automated summarization of settlement exceptions, and AI‑driven decision support.
  • Partner with data engineers and ML practitioners to design and operationalize AI/ML pipelines that consume real‑time trade data and deliver actionable intelligence.
  • Apply advanced data…
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