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 (e.g., 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.