GenAI Developer

Gen AI Developer (5 to 8 years)

The Spec Analytics Intmd Analyst 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 factual 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 C11

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 Spec Analytics Intmd Analyst (C11) in the TTS Analytics team
• The role will report to the AVP or VP leading the team.

The role will involve working on

• Multiple analyses through the year on business problems across the client experience for the TTS business
• This will involve leveraging multiple analytical approaches, tools and techniques, working on multiple data sources (unstructured data like emails, call transcripts, etc., client profile & engagement data, transactions & revenue data, digital data, etc.) to provide data driven insights to business partners and functional stakeholders
• 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.
• Apply GenAI techniques (prompt engineering, RAG, fine‑tuning) and deep learning methods to solve practical user and business problems.
• Collaborate with product, business, operations and data teams to prioritize 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 particular 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.

QUALIFICATIONS

Experience:

• 5-8 years of relevant experience in Data Science (ML and DL) combined with solid experience in Gen AI solution (2 years).
• Demonstrated track record shipping AI‑enabled products to production in an agile environment.

​Must have substantial experience in:

• Identifying and resolving business problems (around client experience and operations) preferably in the financial services industry
• Leveraging and developing analytical tools and methods to identify patterns, trends and outliers in data
• Hands‑on with LLMs and transformer architectures; experience with prompt engineering and system prompt design.
• Working with data from different sources, with different complexities, both structured and unstructured
• 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 within design and development of API-first services in Python and Node.js that expose model inference, feature computation, and analytics.
• Familiarity with graph databases and search infrastructure.

• Agentic AI solution development experience.

Education:

• Masters (preferred) in Computer Science Engineering

This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.

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## Job Family Group:

Decision Management

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## Job Family:

Specialized Analytics (Data Science/Computational Statistics)

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## Time Type:

Full time

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## Most Relevant Skills

Please see the requirements listed above.

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## Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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