Senior Technology Lead, XVA Data Engineering & AI Enablement

Citi

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Jersey City, NJ
Salary
$176,720–$265,080 / yr
Employment
Full-time
Posted
4 days ago
Freshness
Confirmed live today
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $195k
This role $221k
$138k $279k
below market most similar roles pay here above market

This role pays more than 72% of similar roles. Most pay $158,937–$230,100 — the blue band above. At the midpoint, this role pays about $221k versus about $195k for comparable roles.

Based on 240 similar postings.

Employer

About Citi

Citi is one of the world’s most trusted financial institutions, proudly serving millions of customers across the United States.

Citi currently has 273 open roles on FindRole.

Listed pay typically runs $125,760–$188,640 across 256 roles with salary data.

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At a glance

TL;DR · Senior Technology Lead, XVA Data Engineering & AI Enablement

Role Posting Log in to save

The Markets Technology XVA group is seeking a Senior Technology Lead, XVA Data Engineering & AI Enablement - SVP to define and execute the target-state architectural vision for cross-asset X-Value Adjustment valuation, risk, and stress testing platforms. You will lead the modernization of mission-critical pricing and risk calculation pipelines by building high-performance distributed systems and microservices. Key responsibilities include architecting distributed caching, messaging backbones, and polyglot persistence layers while pioneering Agentic AI workflows and MLOps pipelines. You will utilize Java, Spring Boot, Python, Apache Kafka, MongoDB, and vector stores within AWS, GCP, and OpenShift environments. This role solves complex technical problems by translating mathematical models like Monte Carlo simulations into scalable production software while migrating legacy calculation engines to modern cloud-native architectures.

Skills

Java Python Spring Boot AWS GCP OpenShift Kubernetes Docker Apache Kafka MLOps MLflow MongoDB Redis Hazelcast Apache Ignite CI/CD Agentic AI LLM NumPy Pandas Distributed Systems Microservices

What you'll do

  • Define and execute the multi-year architectural blueprint for the global XVA platform to ensure scalability and low latency.
  • Architect high-throughput distributed caching, messaging backbones using Apache Kafka, and polyglot persistence layers.
  • Deploy autonomous Agentic AI systems to optimize pricing workflows, trade reconciliations, and stress testing scenarios.
  • Integrate LLM-driven coding assistants and automated test generation to increase engineering velocity across development pods.
  • Refactor and migrate monolithic legacy calculation engines into modular microservices using Generative AI and code-reasoning models.
  • Implement end-to-end MLOps pipelines for model tracking, validation, and deployment using Docker and Kubernetes.
  • Translate mathematical models like Monte Carlo simulations and Greeks into scalable, production-grade enterprise software.
  • Lead cross-functional engineering pods using scaled Agile methodologies to manage technical debt and production release planning.

What we're looking for

  • 10+ years of progressive software engineering, distributed systems architecture, and technical leadership experience in institutional financial services.
  • 4+ years in a senior leadership or lead architect capacity guiding high-performing, geographically distributed engineering teams.
  • Deep expertise in dual-stack engineering using Java (Spring Boot, Core Java, Concurrency) and Python (AsyncIO, NumPy, Pandas).
  • Proven mastery in designing event-driven architectures, distributed data fabrics, caching systems, and message streaming via Apache Kafka.
  • Proven track record of delivering large-scale enterprise digital transformations and legacy-to-cloud modernization programs.
  • Experience architecting and deploying Agentic AI systems, MLOps pipelines (Docker, Kubernetes/OpenShift, MLflow), and LLM-driven software engineering.
  • Ability to translate mathematical models (SIMM, VaR, Greeks, Monte Carlo) into scalable, production-grade enterprise software.
  • Knowledge of regulatory standards including Basel III/IV, FRTB, and Dodd-Frank.

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