Lead Generative AI Developer

Role overview

Responsibilities

• check_circle 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.
• check_circle Python Development: Develop robust, scalable, and production-ready Python services and APIs that power AI-driven features across COO platforms.
• check_circle 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.
• check_circle MLOps & Deployment: Build and maintain ML/GenAI deployment pipelines using modern MLOps practices, ensuring reliability, observability, and governance.
• check_circle Agentic Workflows: Design and implement multi-agent orchestration frameworks (e.g., LangGraph, Google ADK) for complex, multi-step operational workflows.
• check_circle 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.
• check_circle Data Engineering: Design and optimize data pipelines feeding AI systems, working with vector databases (e.g., Pinecone, Weaviate, pgvector) and enterprise data platforms.
• check_circle Technical Leadership: Mentor junior developers, lead code reviews, and contribute to GenAI standards and best practices across the COO Technology organization.
• check_circle Stakeholder Collaboration: Translate complex business requirements from COO operations stakeholders into technical AI solutions, providing clear communication of trade-offs and timelines.

Basic qualifications

• Experience: 10+ 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
• 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.

Preferred qualifications

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