Build and tune LLM-based applications using platforms like Vertex, GPT, Lamma, Hugging Face, etc.
Create robust prompt engineering strategies and reusable prompt templates.
Integrate generative AI with enterprise applications using APIs, knowledge graphs, vector databases (e.g., PG Vector, Neo4j, Mongodb), and orchestration tools.
Requirements
5+ years of total experience
Strong programming skills in Python, and familiarity with libraries like Transformers, Pandas, scikit-learn, Seaborn, LangChain, LlamaIndex, PyTorch, or TensorFlow.
Experience building applications with OpenAI, Anthropic Claude, Google Gemini, or opensource LLMs.
Working knowledge of retrieval-augmented generation (RAG) pipelines and vector databases.
Understanding of MLOps, model evaluation, prompt tuning, and deployment pipelines.
Build and tune LLM-based applications using platforms like Vertex, GPT, Lamma, Hugging Face, etc.
Create robust prompt engineering strategies and reusable prompt templates.
Integrate generative AI with enterprise applications using APIs, knowledge graphs, vector databases (e.g., PG Vector, Neo4j, Mongodb), and orchestration tools.
Tech Stack
MongoDB
Neo4j
Open Source
Pandas
Python
PyTorch
Scikit-Learn
Tensorflow
Benefits
Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.