This role pays less than
70%
of similar roles. Most pay
$187,750–$267,262
— the shaded band above.
At the midpoint, this role pays about
$195k
versus about
$228k
for comparable roles.
Based on 240 similar postings.
Employer
About Citi
Citi is one of the world’s most trusted financial institutions, proudly serving millions of customers across the United States.
Citi currently has
256 open roles
on FindRole.
Listed pay typically runs
$140,080–$210,120
across 238 roles with salary data.
Collections GenAI Engineering Group Manager, SVP joins the Collections technology team within the Consumer Operations Technology division to lead the design, development, and deployment of intelligent operations and automation platforms. This role involves architecting end-to-end generative AI solutions, including advanced chatbots, agentic workflow automation, and AI-assisted decision platforms. The manager will oversee LLM pipelines involving fine-tuning, prompt engineering, and RAG pipelines using vector databases like Pinecone, Milvus, and Chroma. Key technologies include Python, PyTorch, Hugging Face, FastAPI, Flask, Docker, Kubernetes, and OpenShift. The role requires implementing AI governance frameworks with guardrails such as NeMo Guardrails and Llama Guard to ensure security and compliance. The work focuses on solving complex operational challenges in the financial services sector by building production-ready AI systems that meet strict risk management and regulatory standards for global consumer operations.
Architect and deliver end-to-end generative AI solutions including advanced chatbots and agentic workflow automation.
Engineer LLM pipelines through fine-tuning, prompt engineering, and deployment of open-source and proprietary models.
Build and maintain Retrieval-Augmented Generation (RAG) pipelines using various vector databases to improve model accuracy.
Design and implement AI governance frameworks including guardrails, hallucination detection, and deterministic controls.
Deploy scalable, cloud-native AI infrastructure using Python, Docker, Kubernetes, and OpenShift with CI/CD pipelines.
Ensure all AI models comply with strict financial services risk management, auditability, and data security standards.
Translate complex business challenges into production-ready AI solutions while providing technical direction to cross-functional teams.
What we're looking for
10 or more years of experience in application development or engineering with a focus on AI and machine learning solutions in production environments.
Deep expertise in LLM architectures, embedding models, and orchestration frameworks like Google ADK and LangChain.
Hands-on experience deploying open-source LLMs (Llama, Mistral, Gemma) and using enterprise platforms like Google Vertex AI.
Expert-level Python skills including proficiency with PyTorch, Hugging Face, Pandas, NumPy, FastAPI, and Flask.
Experience implementing deterministic controls, bias detection, and hallucination mitigation in production systems under strict compliance requirements.
Strong experience with containerization (OpenShift, Docker, Kubernetes) and cloud-native application delivery via CI/CD pipelines.
Background in financial services technology including knowledge of products, compliance, and data security standards.
A Bachelor’s degree or equivalent experience is required; a Master's degree in Computer Science or AI is preferred.
Generative AI
LLMs
Agentic AI
RAG
Python
Java
LangChain
AutoGen
Semantic Kernel
Google Vertex AI
OpenShift
Docker
Kubernetes
Microservices
CI/CD
Natural Language Processing
Computer Vision
Deep Learning