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About This Role
Discover your future at Citi
Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact.
Job Overview
The Markets Technology XVA group is seeking a visionary, hands-on, and battle-tested Senior Vice President (SVP) – Senior Technology Lead & AI Enablement.
In this critical leadership role, you will define and execute the target-state architectural vision for Citi’s cross-asset X-Value Adjustment (XVA) valuation, risk, and stress testing platforms (encompassing CVA, DVA, FVA, MVA, and KVA). You will spearhead the modernization of mission-critical pricing and risk calculation pipelines using high-performance distributed systems, modern cloud infrastructure (AWS/GCP/OpenShift), microservices, and cutting-edge AI/LLM-enabled software engineering.
As an engineering and technology leader, you will bridge the gap between high-throughput quantitative computing and modern enterprise application architecture. You will pioneer the integration of Agentic AI workflows, Multi-Agent systems, and MLOps, transforming developer productivity across the SDLC and accelerating legacy system modernization into next-generation real-time valuation frameworks.
Key Responsibilities
Strategic Architecture & Technical Leadership
- Target-State Architecture: Define, champion, and drive the multi-year architecture blueprint for the global XVA platform, ensuring modularity, horizontal scalability, fault tolerance, and ultra-low latency.
- Distributed Computing & Persistence: Architect high-throughput distributed caching, messaging backbones (Apache Kafka), and robust polyglot persistence layers (MongoDB, relational, and vector stores) capable of processing massive intra-day risk simulations.
- Technical Judgment & Systems Thinking: Exercise authoritative technical judgment on framework selection, API contracts, cross-component boundaries, and cloud-native migration patterns (AWS, GCP, Red Hat OpenShift).
- Core Platform Engineering: Maintain deep hands-on fluency in Java / Spring Boot and Python, establishing engineering excellence standards, clean design patterns, and robust automated CI/CD pipelines.
AI Enablement, Agentic Workflows & Legacy Modernization
- Agentic AI & Multi-Agent Frameworks: Architect and deploy autonomous and semi-autonomous Agentic AI systems to optimize complex pricing workflows, trade lifecycle reconciliations, and stress testing scenario generation.
- AI-Assisted SDLC & Developer Productivity: Drive enterprise-grade AI enablement across engineering pods—integrating LLM-driven coding assistants, automated test generation, semantic code search, and architecture review agents to double engineering velocity.
- Legacy Modernization with AI: Leverage Generative AI and code-reasoning models to reverse-engineer, refactor, and migrate monolithic legacy calculation engines onto modular target-state microservices.
- MLOps & Model Governance: Implement end-to-end MLOps pipelines (Docker, Kubernetes/OpenShift, MLflow) for model tracking, validation, deployment, fairness, interpretability, and strict adherence to internal model governance and ethical AI policies.
Program Execution & Quantitative Collaboration
- XVA Strategy & Stress Testing Migration: Lead the delivery of cross-asset XVA technology programs, managing the end-to-end migration of CVA/XVA stress testing engines to the target valuation framework.
- Quant & Desk Partnership: Act as the primary technical partner to Quantitative Analysts (Quants), Financial Engineers, Trading Desks, and Risk Officers, translating mathematical models (SIMM, VaR, Greeks, Monte Carlo simulations) into scalable, production-grade enterprise software.
- Agile Program Governance: Lead cross-functional engineering pods using scaled Agile methodologies; manage technical debt, project milestones, production release planning, and resource allocation across geographically dispersed teams.
Risk, Controls & Regulatory Governance
- Regulatory Compliance: Ensure all technology solutions comply with Basel III/IV, FRTB, Dodd-Frank, and global regulatory standards governing market risk and counterparty valuation.
- Governance & Resiliency: Uphold the highest standards of data integrity, cybersecurity, audit readiness, disaster recovery (HA/DR), and operational resiliency across tier-1 production systems.
- Ethical Judgment: Appropriately assess risk in technical and business decisions, safeguarding the firm's reputation and client assets.
Required Qualifications & Technical Competencies
Leadership & Experience
- 10+ years of progressive software engineering, distributed systems architecture, and technical leadership experience within institutional financial services, tier-1 investment banks, hedge funds, or high-scale technology firms.
- 4+ years in a senior leadership or lead architect capacity guiding high-performing, geographically distributed engineering teams.
- Proven track record of successfully delivering large-scale, enterprise-level digital transformations and legacy-to-cloud modernization programs.
Core Software Engineering & Architecture
- Expertise in Dual-Stack Engineering: Deep expertise in Java (Spring Boot / Core Java / Concurrency) and Python (AsyncIO, NumPy, Pandas).
- Distributed Systems: Proven mastery in designing event-driven architectures, distributed data fabrics, caching systems (e.g., Hazelcast, Redis, Apache Ignite), and message streaming via Apache Kafka.
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Job Family Group:
Technology -
Job Family:
Applications Development -
Time Type:
Full time -
Primary Location:
Jersey City New Jersey United States -
Primary Location Full Time Salary Range:
$176,720.00 - $265,080.00
In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental & vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
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Most Relevant Skills
Please see the requirements listed above. -
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter. -
Anticipated Posting Close Date:
Oct 14, 2026 -
Automated Processing and AI
We use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.
Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision-making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.
Illinois residents – AI Notice and Right
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Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
Salary Context
This $176K-$265K range is above the 75th percentile for Data Engineer roles in our dataset (median: $140K across 33 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 9,079 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Citi, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,000 based on 180 positions with disclosed compensation. This role's midpoint ($220K) sits 25% above the category median. Disclosed range: $176K to $265K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Citi AI Hiring
Citi has 44 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Data Scientist, AI Software Engineer. Positions span Jersey City, NJ, US, Jersey City, NJ, New York, NY. Compensation range: $130K - $300K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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