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Apps Development Senior Manager

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Citi

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Location:
United States , Jersey City

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

142320.00 - 213480.00 USD / Year
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Job Responsibility:

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Requirements:

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

Nice to have:

  • 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.
  • Master’s degree preferred
What we offer:
  • medical, dental & vision coverage
  • 401(k)
  • life, accident, and disability insurance
  • wellness programs
  • paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays

Additional Information:

Job Posted:
May 15, 2026

Expiration:
May 21, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
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