Remoto: Teletrabajo

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. Partners with customers, sales,



engineering and product teams to design, demonstrate and deploy Oracle Cloud architectures that address customer business problems. Drives Oracle Cloud customer consumption by accelerating the adoption of Oracle cloud services including discovery, design and deployment. What you’ll do Discover customer's data challenges, priorities, growth expectations, and success criteria. Design data platforms for integration, storage, processing, and analytics. Recommend data lake, lakehouse, or data warehouse approaches based on each customer’s needs and existing environment. Design batch and real-time pipelines, including ETL/ELT and integration across platforms. Help customers make informed architecture trade-offs across security, data governance, performance, scalability, resilience, operational needs, and cost. Design for reliable operation, including monitoring, failure recovery, and the ability to adapt as demand grows. Lead technical discussions, demonstrations, and proofs of concept, and help customers bring validated solutions into production. Work with customer and internal teams to resolve integration and operational challenges and support long-term adoption. Build the data foundation for analytics, machine learning, and generative AI use cases when relevant. What we’re looking for 8+ years of experience in data engineering, data architecture, or related roles, including hands‑on experience designing enterprise data platforms that scale with demand, recover from failures, and operate reliably in production. Practical knowledge of distributed processing, data integration, and modern analytics architectures. Experience with technologies such as Spark, Databricks, Snowflake, BigQuery, or cloud data services. The ability to connect architecture decisions to business outcomes and explain trade-offs to different audiences. #J-18808-Ljbffr

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