Apps Dev Tech Lead Analyst

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., GitHub Copilot or equivalent GenAI tools) 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 DevOps 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 integration patterns to ensure high-quality, low-latency data flows across platform components, supporting both operational and analytical AI use cases.
• Evaluate and adopt AI/ML frameworks and tooling appropriate for high-throughput financial systems, ensuring models are production-grade, explainable, and compliant with risk and governance standards.
• Champion responsible AI practices — ensuring fairness, auditability, and regulatory alignment in all AI-driven platform capabilities.
• Drive the team's AI adoption roadmap — identifying high-value opportunities to apply GenAI, ML, and intelligent automation to real platform engineering and operational challenges.

4. Platform & Domain Expertise

• Develop deep understanding of equity trade lifecycle, settlement mechanics, and cash securities processing to make informed technical decisions aligned with business needs.
• Partner with business analysts, product owners, and operations teams to translate complex settlement workflows and regulatory requirements into robust technical solutions.
• Ensure platform components meet SLA, SLO, and SLI targets for availability, throughput, and latency in a mission-critical environment.
• Collaborate with downstream and upstream system owners across the Cash Securities Settlements ecosystem to ensure seamless integration and data integrity.
• Drive continuous platform modernization — improving maintainability, scalability, and operational efficiency of TMZ components.

5. Technical Leadership & People Management

• Lead, mentor, and grow a team of engineers — setting high engineering standards and fostering a culture of craftsmanship, AI curiosity, accountability, and continuous learning.
• Mentor engineers on AI tool usage, prompt engineering, and responsible AI practices, building internal AI literacy across the team.
• Partner with architects, platform engineers, and cross-functional teams to design scalable, distributed, and AI-ready systems.
• Collaborate closely with DevOps, SRE, and infrastructure teams to optimize deployments, observability, and production resilience — leveraging AIOps capabilities.
• Lead technical discussions, architecture reviews, and design sessions — providing clear guidance on engineering decisions, AI integration patterns, and trade-offs.
• Drive capacity planning, technical roadmap execution, and engineering delivery commitments for the TMZ platform.
• Represent the engineering team in stakeholder forums, providing transparent updates on delivery progress, risks, and technical health.

Skills and Qualifications

Must-Have Skills

1. Programming Languages

• Kotlin Primary language for platform services; strong hands-on proficiency required
• Python Used for data pipelines, AI/ML integration, scripting, and automation
• Java Core backend development; deep expertise in production-grade Java applications

2. System Architecture — Real-Time, High-Throughput & Performance Optimization

• Microservices Architecture Design and delivery of loosely coupled, independently deployable services at scale
• Event-Driven & Messaging Systems Hands-on experience with Kafka or Solace for real-time, high-throughput event streaming and messaging
• Low-Latency & High-Performance Computing Proven experience optimizing systems for sub-millisecond to millisecond response times in high-volume financial environments
• High Availability & Fault Tolerance Design patterns for resilient systems — circuit breakers, bulkheads, failover, and graceful degradation
• Databases Strong proficiency in Oracle (SQL) for transactional data and MongoDB (NoSQL) for flexible, high-throughput data models

3. Advanced Data & AI Integration

• AI & ML Integration Experience integrating AI/ML models into production systems — including model serving, inference pipelines, and real-time scoring
• GenAI Tooling Hands-on experience with AI-assisted development tools (GitHub Copilot, or equivalent) and LLM API integration
• Data Engineering Strong understanding of data pipelines, streaming data processing, and data quality patterns in high-volume environments
• Intelligent Automation Applying ML to automate exception handling, anomaly detection, and operational workflows in financial platforms
• AI Governance Familiarity with responsible AI principles — explain ability, auditability, and compliance in regulated environments

4. Cloud & DevOps — Citi Engineering Excellence Standards

• Cloud-Native Engineering Hands-on experience with AWS, Kubernetes, and Docker for scalable, containerized deployments
• CI/CD Pipelines Strong proficiency in building and maintaining CI/CD pipelines aligned to Citi Engineering Excellence Standards
• Trunk-Based Development Feature flags, progressive delivery, and continuous integration as core delivery practices
• Observability & Monitoring Experience with production monitoring, distributed tracing, intelligent alerting, and SRE practices
• Secure Engineering AI-augmented vulnerability assessments, secure coding standards, and compliance in regulated financial environments
• Agile Delivery Strong experience in Agile/SAFe frameworks — backlog management, sprint delivery, and cross-team dependency management

5. Good-to-Have Skills

• Experience with real-time ML model serving in low-latency, high-throughput environments (e.g., feature stores, online inference).
• Familiarity with Retrieval-Augmented Generation (RAG), vector databases (e.g., Pinecone, Weaviate), or AI agent frameworks (e.g., LangChain, AutoGen).
• Knowledge of MLOps practices — model versioning, deployment pipelines, and monitoring in production.
• Understanding of AIOps — AI-driven incident management, anomaly detection, and intelligent observability.
• Knowledge of equity trade lifecycle, settlement mechanics, or cash securities processing.
• Understanding of regulatory compliance frameworks relevant to securities settlement (e.g., T+1, CSDR).
• Experience with performance profiling and tuning of JVM-based applications (Kotlin/Java) under high load.
• Knowledge of risk management, reconciliation, and exception handling patterns in settlement workflows.
• Familiarity with prompt engineering and fine-tuning strategies for LLMs in an enterprise context.

What We Are Looking For

This role is for an engineer at heart who has grown into a leader — someone who:

• Stays close to the code — actively participates in design, review, and technical problem-solving alongside the team.
• Thinks AI-first — actively seeks opportunities to apply AI and GenAI to engineering and operational challenges, not just product features.
• Owns outcomes — takes full accountability for platform quality, reliability, and delivery commitments.
• Thrives in complexity — comfortable navigating the technical and operational complexity of mission-critical financial systems.
• Raises the bar — consistently pushes for higher engineering standards, better test coverage, cleaner architecture, and more resilient systems.
• Embraces AI pragmatically — identifies high-value opportunities to apply AI/ML to real platform problems, without losing sight of engineering fundamentals.
• Inspires curiosity — creates an environment where engineers are excited to learn, experiment, and grow with AI.
• Builds great teams — invests in people, creates psychological safety, and develops the next generation of engineering talent.

Education:

• Bachelor’s degree/University degree or equivalent experience
• Master’s degree preferred