NLP ML Engineer for Finance & Document Processing

Jobcubby; Buenos Aires; Presencial; ARS 2.000.000 - 3.000.000; Jornada completa

Descripción de la vacante

Jobcubby is seeking a Machine Learning Engineer in Buenos Aires to design NLP-powered document understanding systems, build data pipelines, and deploy models via REST APIs. You will work with Python, Pandas, NumPy, and scikit-learn, applying NLP with spaCy/NLTK/transformers and leverage OCR for data extraction.

Ideal candidates have 2–5 years in ML/NLP, experience in finance or fintech domains, and familiarity with Azure ML and Docker for deployment.

Formación

• Strong Python expertise with Pandas, NumPy, and scikit-learn.
• Experience with NLP libraries such as spaCy, NLTK, or transformers.
• Knowledge of fuzzy matching algorithms and document processing.
• Experience deploying ML models via REST APIs using Docker/Azure ML.
• Familiarity with OCR, PDF parsing, and Excel data extraction.
• Understanding of supervised and unsupervised learning techniques.
• Experience with Large Language Models for document understanding.

Responsabilidades

• Develop and implement ML models for document understanding and processing.
• Utilize Python libraries for data manipulation and analysis.
• Apply NLP techniques using spaCy, NLTK, or transformers.
• Implement fuzzy matching algorithms for data accuracy.
• Process documents using OCR, PDF parsing, and Excel data extraction.
• Deploy ML models as REST APIs using Docker and Azure ML.
• Design and manage data pipelines and ETL processes.
• Collaborate with cross-functional teams to meet project goals.

Conocimientos

Python

NLP

Fuzzy matching

Document processing

ML deployment

DL frameworks

LLMs for docs

Azure ML

Data pipelines

Finance domain

Educación

Master’s degree in Computer Science or Data Science

Herramientas

Docker

Azure ML

OCR tooling

Descripción del empleo

Jobcubby is seeking a Machine Learning Engineer in Buenos Aires to design NLP-powered document understanding systems, build data pipelines, and deploy models via REST APIs. You will work with Python, Pandas, NumPy, and scikit-learn, applying NLP with spaCy/NLTK/transformers and leverage OCR for data extraction.

Ideal candidates have 2–5 years in ML/NLP, experience in finance or fintech domains, and familiarity with Azure ML and Docker for deployment.