1040 - Ai Engineer · Senior · Latam

Darwoft; Quilmes; On-site; ARS 1,200,000 - 2,200,000; Full time

Job summary

Darwoft is looking for a Machine Learning Engineer in Buenos Aires to advance NLP-powered document understanding for fintech applications. You will build models in Python, apply NLP techniques, and deploy APIs using Docker and Azure ML.

You will handle data pipelines, OCR processing, and collaboration with cross-functional teams to deliver accurate reconciliation and accounting insights. The ideal candidate has 2–5 years in ML/NLP with finance domain experience and a Master’s degree.

Qualifications

• Master's degree in a relevant field and 2–5 years of ML/NLP experience.
• Strong programming in Python with NLP focus and data processing libraries.
• Experience with document processing, OCR, and data extraction.
• Experience deploying models as REST APIs using Docker and cloud services.

Responsibilities

• Develop ML models for document understanding and processing.
• Use Python libraries (Pandas, NumPy, scikit-learn) for data work.
• Apply NLP techniques with spaCy, NLTK, or transformers.
• Implement fuzzy matching for data accuracy.
• Process documents via OCR, PDF parsing, and Excel extraction.
• Deploy models as REST APIs using Docker and Azure ML.
• Work with supervised and unsupervised learning approaches.
• Design data pipelines and ETL processes.
• Collaborate with cross-functional teams to meet project goals.

Skills

Python (Pandas/Numpy)

NLP libraries

Fuzzy matching

OCR/Document processing

ML model deployment

Supervised/unsupervised learning

Deep learning (TensorFlow/PyTorch)

LLMs for document understanding

Azure ML / Cognitive Services

Finance/Fintech domain

2-5 years in ML/NLP

Education

Master’s degree in Computer Science / Data Science

Tools

Docker

Azure ML

REST APIs

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.