Need to recruit 2 freelancers
Budget: $250 – $750 USD
Role Overview: (Submit CVs without external contact)
We are seeking a highly skilled AI/NLP Specialist to design, develop, and implement advanced AI models and NLP workflows. The ideal candidate will leverage cutting-edge techniques such as Retrieval-Augmented Generation (RAG) to extract, analyze, and interpret insights from structured and unstructured data, including reports, documents, and real-time feeds. This role is essential for driving automation and delivering actionable insights in our data workflows.
Key Responsibilities:
AI/NLP Model Development:
Design and implement NLP models to analyze large-scale documents, reports, and filings.
Extract relevant information and insights aligned with business needs.
RAG System Implementation:
Develop RAG workflows combining Large Language Models (LLMs) with external knowledge bases to enhance accuracy and relevance.
Data Automation:
Build AI-driven solutions to automate the analysis of unstructured data, including extracting critical data points from filings, reports, and commentaries.
Integration and Collaboration:
Work closely with DAQ/DAP teams to integrate NLP pipelines into broader data workflows for seamless processing.
Model Fine-Tuning:
Fine-tune and adapt LLMs (e.g., GPT, BERT) for specific use cases, ensuring compliance with industry and organizational needs.
We are seeking a highly skilled AI/NLP Specialist to design, develop, and implement advanced AI models and NLP workflows. The ideal candidate will leverage cutting-edge techniques such as Retrieval-Augmented Generation (RAG) to extract, analyze, and interpret insights from structured and unstructured data, including reports, documents, and real-time feeds. This role is essential for driving automation and delivering actionable insights in our data workflows.
Key Responsibilities:
AI/NLP Model Development:
Design and implement NLP models to analyze large-scale documents, reports, and filings.
Extract relevant information and insights aligned with business needs.
RAG System Implementation:
Develop RAG workflows combining Large Language Models (LLMs) with external knowledge bases to enhance accuracy and relevance.
Data Automation:
Build AI-driven solutions to automate the analysis of unstructured data, including extracting critical data points from filings, reports, and commentaries.
Integration and Collaboration:
Work closely with DAQ/DAP teams to integrate NLP pipelines into broader data workflows for seamless processing.
Model Fine-Tuning:
Fine-tune and adapt LLMs (e.g., GPT, BERT) for specific use cases, ensuring compliance with industry and organizational needs.