ML - Newyork, NY - Citi

Photon•United States,

14h•$155,000 - $217,000

About The Position

Citi is seeking an experienced GenAI/Python Developer to design, build, and deploy production-grade Generative AI applications and intelligent agent-based solutions. The role focuses on scalable AI solutions, integration of commercial and open-source LLMs, model fine-tuning, and optimization of applications for performance, latency, cost, reliability, and production scalability. The developer will build GenAI workflows, RAG solutions, retrieval pipelines, backend services, and APIs; integrate and evaluate LLMs; and deploy and maintain solutions across cloud environments. The position involves collaboration with engineering, AI/ML, architecture, and product teams, as well as troubleshooting and optimizing production AI applications. The listed compensation range is $155,000–$217,000 USD, based on the candidate’s experience and qualifications. Full-time employees may receive medical, vision, and dental benefits, a 401(k) retirement plan, variable pay or incentives, paid time off, and paid holidays. The position is not available to independent contractors.

Requirements

Experience as a GenAI/Python Developer or in a comparable role, with hands-on experience building scalable AI solutions.
Expertise in Python, LangGraph, LangChain, LlamaIndex, RAG, vector databases, LLM integration, and multi-agent architectures.
Experience integrating commercial and open-source LLMs.
Experience fine-tuning models and optimizing GenAI applications for performance, latency, cost, reliability, and production scalability.
Experience with Python, FastAPI, and asynchronous programming.
Experience with PyTorch, TensorFlow, Hugging Face, LoRA, and QLoRA for fine-tuning and customizing LLMs.
Experience with Docker and CI/CD pipelines.
Experience deploying and maintaining GenAI solutions in cloud environments such as AWS, Azure, or GCP.

Responsibilities

Design, develop, and orchestrate Generative AI workflows using LangGraph, LangChain, and multi-agent architectures.
Build and maintain Retrieval-Augmented Generation (RAG) solutions using LlamaIndex and vector databases.
Design retrieval pipelines to improve the accuracy and relevance of LLM-generated responses.
Integrate and evaluate commercial and open-source LLMs, including OpenAI, Anthropic, Llama, and Mistral, according to application, performance, and cost requirements.
Develop scalable, high-performance backend services and APIs using Python, FastAPI, and asynchronous programming.
Fine-tune and customize LLMs for domain-specific use cases using PyTorch, TensorFlow, Hugging Face, LoRA, and QLoRA.
Apply prompt engineering techniques to improve response quality and application performance.
Optimize GenAI workflows for latency, cost, scalability, and reliability.
Implement monitoring and observability for LLM applications using tools such as LangSmith and Langfuse.
Containerize AI applications and services using Docker and support production deployments through CI/CD pipelines.
Deploy and maintain GenAI solutions across AWS, Azure, or GCP cloud environments.
Collaborate with engineering, AI/ML, architecture, and product teams to translate business requirements into scalable AI solutions.
Troubleshoot and optimize production AI applications, APIs, retrieval pipelines, and model integrations.