Senior Ml Data Engineer — Remote

Caramel Point; Buenos Aires; Remote; ARS 2,000,000 - 4,500,000; Full time

Job summary

Caramel Point in Buenos Aires seeks a Machine Learning Engineer focused on NLP to advance document understanding in fintech. You will build NLP models, leverage Python libraries, and deploy REST APIs.

The role requires 2-5 years of ML experience in finance, expertise with spaCy, transformers, OCR, and Azure ML, and a Master’s in CS/Data Science.

Qualifications

• 2–5 years of ML engineering experience, preferably in NLP or financial data.
• Strong Python skills with Pandas, NumPy, and scikit-learn.
• Experience with NLP libraries: spaCy, NLTK, transformers.
• Knowledge of fuzzy matching algorithms and document processing (OCR, PDF parsing).
• ML model deployment via REST APIs; Docker and Azure ML familiarity.
• Understanding of supervised and unsupervised learning.
• Experience with LLMs for document understanding.
• Familiarity with Azure Cognitive Services.
• Finance/fintech domain experience (reconciliation, accounting).

Responsibilities

• Develop ML models for document understanding and processing.
• Apply NLP techniques using spaCy, NLTK, and transformers.
• Implement fuzzy matching for data accuracy.
• Process documents with OCR, PDF parsing, and Excel extraction.
• Deploy models as REST APIs using Docker and Azure ML.
• Collaborate with cross-functional teams.

Skills

Python

Pandas

NumPy

scikit-learn

NLP

spaCy

NLTK

Transformers

Fuzzy matching

OCR

PDF parsing

Excel data extraction

REST APIs

Docker

Azure ML

TensorFlow

PyTorch

LLMs

Azure Cognitive Services

Finance domain

Education

Master’s degree in Computer Science, Data Science, or related field

Tools

Pandas

NumPy

scikit-learn

Job description

We are seeking a Machine Learning Engineer with strong expertise in Python and a focus on Natural Language Processing (NLP) to join our team. The ideal candidate will have experience in the finance or fintech domain, specifically in reconciliation and accounting.

Responsibilities:

Develop and implement machine learning models for document understanding and processing.
Utilize Python libraries such as Pandas, NumPy, and scikit-learn for data manipulation and analysis.
Apply NLP techniques using libraries like spaCy, NLTK, or transformers.
Implement fuzzy matching algorithms (., Levenshtein distance, record linkage) for data accuracy.
Process documents using OCR, PDF parsing, and Excel data extraction methods.
Deploy machine learning models as REST APIs using Docker and Azure ML.
Understand and apply supervised and unsupervised learning techniques.
Design and manage data pipelines and ETL processes.
Collaborate with cross-functional teams to meet project goals.

Required Skills:

Strong expertise in Python (Pandas, NumPy, scikit-learn).
Experience with NLP libraries (spaCy, NLTK, transformers).
Knowledge of fuzzy matching algorithms.
Document processing skills (OCR, PDF parsing, Excel extraction).
Experience in ML model deployment (REST APIs, Docker, Azure ML).
Understanding of supervised/unsupervised learning techniques.
Familiarity with deep learning frameworks (TensorFlow, PyTorch).
Experience with Large Language Models (LLMs) for document understanding (OpenAI, Azure OpenAI).
Knowledge of Azure ML services and Azure Cognitive Services.
2-5 years of experience in ML engineering, focusing on NLP or financial data processing.
Experience in the finance/fintech domain (reconciliation, accounting).

Education:

Master’s degree in Computer Science, Data Science, or a related field.

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The Executive Director, Data Engineering is the functional leader for network data within Global Network Services, directly leading a 25 person engineering organization focuses on Network Data. Data is the foundation layer of the platforms we build