Discover your future at Citi
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Job Overview
The role will involve working on
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.
Experience:
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.
Must have substantial experience in:
5+ years, building production web applications and services with Python.
Front‑end development with Angular; 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.
Education:
Bachelor / 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------------------------------------------------------
Job Family:
Specialized Analytics (Data Science/Computational Statistics)------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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