Senior Ml Data Engineer — Remote

Caramel Point; Quilmes; Hybrid; ARS 2,000,000 - 3,600,000; Full time

Job summary

Caramel Point seeks a Machine Learning Engineer focused on NLP and Python to drive document understanding and reconciliation workflows in a fintech context. You will build NLP pipelines, apply fuzzy matching, and process documents with OCR, PDF parsing, and Excel extraction.

The role involves deploying models via REST APIs using Docker and Azure ML, exploring supervised and unsupervised learning, and working with TF/PyTorch and large language models.

Qualifications

• Master's degree required in Computer Science, Data Science, or related field.
• 2–5 years of ML engineering experience focusing on NLP or financial data processing.
• Experience in finance/fintech domain (reconciliation, accounting) is a plus.

Responsibilities

• Develop and implement ML models for document understanding and processing.
• Apply NLP techniques and libraries to extract meaning from text.
• Implement fuzzy matching and document processing (OCR, PDF parsing, Excel extraction).
• Deploy ML models as REST APIs using Docker and Azure ML.
• Collaborate with cross-functional teams to meet project goals.

Skills

Python

Pandas

NumPy

scikit-learn

NLP

spaCy

NLTK

transformers

Fuzzy matching

OCR

PDF parsing

Excel extraction

REST APIs

Docker

Azure ML

Supervised learning

Unsupervised learning

TensorFlow

PyTorch

LLMs

OpenAI

Azure OpenAI

Azure Cognitive Services

Data processing

Education

MSc in CS/DS

Tools

spaCy

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