AI Developer for Finance: RAG & LLM Solutions Architect

SystemsAccountants; New York (NY); On-site; USD 140,000 - 180,000; Full time

Job summary

SystemsAccountants in New York seeks an experienced AI Consultant to design, build, and deploy advanced AI solutions within finance and analytics contexts. You will develop LLM-powered applications, RAG pipelines, and ML models that tackle real-world business problems.

You will collaborate with stakeholders to translate requirements into scalable AI/ML deployments, implement API-based integrations (e.g., FastAPI), and optimize data workflows across unstructured data, embeddings, and vector

Qualifications

• Experience with Retrieval-Augmented Generation (RAG) architectures and LLM integration.
• Strong working knowledge of LLMs, including tokenization, embeddings, vector databases, and unstructured data processing.
• Experience with traditional machine learning models such as Gradient Boosting, XGBoost, Classification, and Regression.
• Solid programming experience in Python and familiarity with AI/ML development workflows.

Responsibilities

• Design and develop RAG pipelines leveraging OpenAI models, LangChain, and/or Llama-based frameworks.
• Build and optimize LLM-driven applications, including prompt engineering, tokenization strategies, and management of large-scale unstructured data.
• Develop, fine-tune, and deploy machine learning models, including classification and regression techniques, for business-critical use cases.
• Collaborate with stakeholders to translate business requirements into scalable AI and ML solutions.
• Support API-based model deployment and integration (e.g., using FastAPI) in production environments.

Skills

RAG architectures

LLM integration

Python

Vector databases

Embeddings

Unstructured data

ML algorithms

Model deployment

Job description

SystemsAccountants in New York seeks an experienced AI Consultant to design, build, and deploy advanced AI solutions within finance and analytics contexts. You will develop LLM-powered applications, RAG pipelines, and ML models that tackle real-world business problems.

You will collaborate with stakeholders to translate requirements into scalable AI/ML deployments, implement API-based integrations (e.g., FastAPI), and optimize data workflows across unstructured data, embeddings, and vector