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 adecuado 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.

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: automation, observability, analytics, and AI all depend on trustworthy, queryable network state. This leader owns the network data platform end to end, from sourcing and modelling through to the serving layer that engineering teams consume, and sets the strategy for how network data is governed, delivered, and continuously improved at scale.

In parallel, the Executive Director serves as the Argentina (Buenos Aires) Regional Lead for Infrastructure Platform Foundational Services (Network, Storage, DataCentre, Data Protection and Recovery). Accountable for site strategy, talent development, governance, executive presence, and operational alignment across the region. The role establishes Argentina as a strategic engineering hub by building and sustaining high-performing engineering teams aligned to global priorities and outcomes.

This leader partners closely with the automation platform, SRE, and Product Engineering teams,



and with firm-wide data governance and data quality programmes, to ensure network data is well governed and fit for production use.

Key Responsibilities

Functional Responsibilities - Data Engineering

- Define and execute the network data strategy, covering data modelling, authoritative sources, distribution, and how data is made available to consuming platforms
- Build and operate the data platform: ingestion and streaming pipelines, transformation, storage and modelling, and performant serving APIs and self-service access for engineering teams
- Establish consistent data models and identity standards for network entities (device, circuit, site, interface) so data can be reliably joined and reused across systems
- Drive data quality as an engineering discipline: validation, automated testing, monitoring, and measurable quality targets across the data lifecycle
- establish data contracts and data SLOs covering freshness, completeness, accuracy, and availability, with clear ownership, versioning, and schema-evolution standards
- Implement lineage and observability across data pipelines so issues can be traced to source and change impact can be assessed ahead of time
- Partner with firm-wide data governance and data quality programmes to meet audit, risk, and regulatory obligations
- Make network data AI-ready: structured, well-described, and accessible so agentic and LLM-driven capabilities can reason over accurate state, and apply AI within the data lifecycle itself
- Establish engineering standards, testing frameworks, and CI/CD pipelines so data pipelines and services are production-grade, secure-by-design, and observable

Del

📌 Senior Ml Data Engineer: Cost Analytics (Quilmes)
🏢 Caramel Point
📍 Quilmes