Senior Generative AI Developer

We are looking for a  Senior Generative AI Developer  to join our COO Technology Division in New York. In this high-impact role, you will architect, develop, and operationalize cutting-edge Generative AI and Large Language Model (LLM) solutions that directly transform how Citi's operational teams work. You will collaborate with cross-functional stakeholders - including operations leads, data engineers, product managers, and enterprise architects to deliver enterprise-grade AI capabilities at scale. This is a hands-on engineering role for a builder who thrives at the intersection of applied AI research and production software engineering.

Design & Build GenAI Solutions:  Architect and implement end-to-end Generative AI pipelines including LLM integrations, Retrieval-Augmented Generation (RAG) systems, autonomous AI agents, and prompt engineering frameworks. Python Development:  Develop robust, scalable, and production-ready Python services and APIs that power AI-driven features across COO platforms. Model Integration & Fine-tuning:  Evaluate, integrate, and fine-tune LLMs (e.g., GPT-5, Claude, Mistral) and embedding models for domain-specific financial use cases. MLOps & Deployment:  Build and maintain ML/GenAI deployment pipelines using modern MLOps practices, ensuring reliability, observability, and governance. Agentic Workflows:  Design and implement multi-agent orchestration frameworks (e.g., LangGraph, Google ADK) for complex, multi-step operational workflows. Enterprise AI Governance:  Collaborate with Citi's AI Risk and Compliance teams to ensure all AI solutions align with regulatory requirements, responsible AI frameworks, and data privacy standards. Data Engineering:  Design and optimize data pipelines feeding AI systems, working with vector databases (e.g., Pinecone, Weaviate, pgvector) and enterprise data platforms. Technical Leadership:  Mentor junior developers, lead code reviews, and contribute to GenAI standards and best practices across the COO Technology organization. Stakeholder Collaboration:  Translate complex business requirements from COO operations stakeholders into technical AI solutions, providing clear communication of trade-offs and timelines.

Experience:  6+ years of professional software engineering experience, with at least  2+ years focused on Generative AI / LLM application development . Python:  Expert-level Python proficiency — including async programming, API development (FastAPI, Flask), and software design patterns. GenAI & LLM Stack: Deep hands-on experience with LLM frameworks:  LangChain, LangGraph, LlamaIndex etc Hands on experience with Google Cloud AI Platform Proven experience with  RAG architectures , embedding pipelines, and vector search Strong understanding of  prompt engineering, few-shot learning, and chain-of-thought  techniques Experience integrating with LLM APIs:  OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, or Google Vertex AI Machine Learning:  Solid grounding in ML fundamentals; familiarity with model evaluation, fine-tuning (LoRA, PEFT), and inference optimization. Cloud Platforms:  Hands-on experience with at least one major cloud provider —  AWS, Azure, or GCP  — particularly managed AI/ML services. Data & Databases:  Proficiency with SQL, NoSQL, and  vector databases  (Pinecone, Weaviate, Chroma, pgvector). Software Engineering Practices:  Strong understanding of CI/CD pipelines, containerization ( Docker, Kubernetes ), version control (Git), and automated testing. Financial Services Acumen (Preferred):  Prior experience in banking, fintech, or a regulated industry is a strong plus.

Experience with  multi-agent orchestration  frameworks (MS AgentFramework, ADK, Strands, LangGraph) Familiarity with  MLflow, Weights & Biases, or similar  experiment tracking and model management tools Knowledge of  responsible AI practices : bias