Citi
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ROLE DESCRIPTION The role will be AVP (C12) in the TTS Analytics team.
Overview
The C12 is a developing professional role. Deals with most problems independently and has some latitude to solve complex problems. Integrates in-depth specialty area knowledge with a solid understanding of industry standards and practices. Good understanding of how the team and area integrate with others in accomplishing the objectives of the sub function/ job family. Applies analytical thinking and knowledge of data analysis tools and methodologies. Requires attention to detail when making judgments and recommendations based on the analysis of information. Typically deals with variable issues with potentially broader business impact. Applies professional judgment when interpreting data and results. Breaks down information in a systematic and communicable manner. Developed communication and diplomacy skills are required to exchange potentially complex/sensitive information. Moderate but direct impact through close contact with the businesses' core activities. Quality and timeliness of service provided will affect the effectiveness of own team and other closely related teams.
TTS Analytics C12
What do we do?
The TTS Analytics team provides analytical insights to the Product, Pricing, Client Experience and Sales functions within the global Treasury & Trade Services business. The team works on business problems focused on enhancing client experience, driving acquisitions, cross-sell and revenue growth.
The team extracts relevant insights, identifies business opportunities, converts business problems into analytical frameworks, uses big data tools and AI/ML techniques to drive data driven business outcomes in collaboration with business and product partners.
The team works on building and operating Generative AI and deep learning solutions - Design, implement, and scale production‑grade AI applications end‑to‑end—from data ingestion and model services to user‑facing interfaces, observability, and secure deployments.
ROLE DESCRIPTION
The role will be AVP (C12) in the TTS Analytics team.
As a key contributor to ideation on analytical projects to tackle strategic business priorities.
The role will require endless curiosity, as ambiguity and open-ended questions are a core part of the team’s work.
Own end‑to‑end delivery of AI‑powered products: requirements, design, implementation, testing, deployment, and support.
Design and build API‑first services (REST/GraphQL) in Python and Node.js that expose model inference, feature computation, and analytics.
Develop intuitive front‑end interfaces and internal tools using React/Angular/Vue to operationalize model insights and user workflows.
Implement containerized services with Docker; automate build/test/deploy via CI/CD (Tekton, Harness, Git‑based pipelines).
Use Ansible for configuration management, environment provisioning, and repeatable deployments across Linux/Windows.
Establish secure‑by‑design practices (authentication/authorization, secret management, data access controls) and enforce coding standards.
Instrument applications and pipelines with monitoring and logging; drive performance tuning, memory management, and cost optimization.
Apply GenAI techniques (prompt engineering, RAG, fine‑tuning) and deep learning methods to solve practical user and business problems.
Build evaluation harnesses and guardrails for LLM quality, safety, hallucination reduction, and bias assessment; iterate based on telemetry.
Collaborate with product, data, security, and platform teams to prioritize roadmaps and translate ambiguous problems into delivered capabilities.
Create clear technical documentation, architecture diagrams, and runbooks; participate in design and code reviews.
Appropriately assess risk when business decisions are made, demonstrating consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.
Ability to build partnerships with cross-function leaders.
Min 8 years of relevant experience in full stack development and Data Science (ML and DL) combined.
Demonstrated track record shipping AI‑enabled products to production in an agile environment.
8+ years, building production web applications and services with Python and Node.js.
Front‑end development with React/Angular/Vue; strong TypeScript/JavaScript fundamentals.
API design and development with REST and GraphQL; experience with microservices patterns.
Datastores: SQL (e.g., PostGREs) and NoSQL (e.g., MongoDB); schema/data modeling, indexing, and performance tuning.
Containers with Docker; CI/CD using Tekton, Harness, and Git‑based pipelines for automated testing and deployments.
Configuration management and automation with Ansible; scripting for environment provisioning and release management.
Operating systems: Linux and Windows; solid understanding of OS/process fundamentals.
Networking basics: DNS, load balancers, firewalls, routing, ports, and protocols.
Observability: application monitoring, centralized logging, tracing; strong debugging and problem‑resolution skills.
Security: authentication, authorization, secret management, secure coding and dependency hygiene.
Generative AI & Deep Learning (Complementary and Strongly Preferred)
Hands‑on with LLMs and transformer architectures; experience with prompt engineering and system prompt design.
Retrieval‑Augmented Generation (RAG): embeddings, vector indexes, chunking strategies, and retrieval evaluation.
Model customization: fine‑tuning/LoRA/PEFT; data curation, labeling, and experiment tracking for reproducibility.
Frameworks and tooling: PyTorch/TensorFlow, Hugging Face ecosystem, and popular orchestration libraries.
Model serving/inference optimization: batching, token streaming, quantization, caching, and concurrency controls.
Quality & safety: automatic evaluation, red‑teaming, toxicity filters, PII handling, prompt injection defenses.
MLOps for GenAI: feature pipelines, model registries, rollout strategies (A/B, shadow), monitoring for drift and hallucinations.
Good to Have
Experience with enterprise identity and access management, secrets vaults, and compliance controls.
Knowledge of data privacy and responsible AI practices; experience implementing audit and guardrail tooling.
Familiarity with vector databases and search infrastructure.
Bachelor / Masters (preferred) in Computer Science Engineering.
#LI-KR5
Decision Management
Specialized Analytics (Data Science/Computational Statistics)
Full time
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View Citi’s EEO Policy Statement and the Know Your Rights poster.
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