Senior Generative AI Developer

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., Lang Graph, 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:
  Lang Chain, Lang Graph, Llama Index 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, No
  
  SQL, 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 Agent Framework, ADK, Strands, Lang Graph)
• Familiarity with MLflow, Weights & Biases, or similar experiment tracking and model management tools
• Knowledge of responsible AI practices : bias detection, explainability, hallucination mitigation
• Exposure to Kafka, Spark, or Airflow for data pipeline engineering
• Experience working in an Agile/SAFe delivery environment
• Advanced degree (M.S.) in Computer Science, AI/ML, or a related discipline — or equivalent demonstrated experience

Languages :
Python (expert), SQL, Bash

GenAI Frameworks :
Lang Chain, llama

Index, Lang Graph, Semantic Kernel

LLM Providers :
OpenAI / Azure OpenAI, Anthropic, AWS Bedrock, Google Vertex AI

Vector Databases :
Pinecone, Weaviate, pgvector, Chroma

Cloud : AWS / Azure / GCP

MLOps : MLflow, Docker, Kubernetes, Git Hub Actions

Data Engineering :
Spark, Airflow, Kafka

Databases :
PostgreSQL, MongoDB, Redis

Education:

• Bachelor's degree/University degree or equivalent experience
• Master's degree preferred