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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Generative AI Developer - **Company:** Citi - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $142,320.0 - $213,480.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Bash Shell, Cloud Computing, Code Review, Encodings, Computer Programming, Databases, Information Engineering, Software Design Patterns, Github, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, NoSQL, Redis, Azure Machine Learning, Search Technologies, Software Engineering, SQL Databases, Systems Integration, Enterprise Data Management, Google Cloud, Flask (Web Framework), Delivery Pipeline, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Model Validation, Generative AI, Git, Fastapi, Build Management, Containerization, AI Platforms, Kubernetes, Information Technology, Apache Kafka, Machine Learning Operations, Api Design, GPT, Software Version Control, Data Pipelines, Docker - **Published:** August 7, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=57ffffef4925474d ## About the Role * 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 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 Technical Stack (Working Knowledge Expected) Languages: Python (expert), SQL, Bash GenAI Frameworks: LangChain, LlamaIndex, LangGraph, 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, GitHub Actions Data Engineering: Spark, Airflow, Kafka Databases: PostgreSQL, MongoDB, Redis Education: * Bachelor's degree/University degree or equivalent experience * Master's degree preferred ## Description 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. ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) - [Lessons Learned Building a GenAI Powered App](https://www.wearedevelopers.com/videos/1156-lessons-learned-building-a-genai-powered-app) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Use Generative AI to Accelerate Learning to Code](https://www.wearedevelopers.com/magazine/530-how-to-use-generative-ai-to-accelerate-learning-to-code) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)