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$ cat jobs/data-scientist-applied-ai-latin-america-remote-azumo-8d6d7e84a7e1.json

Data Scientist, Applied AI - Latin America - Remote

azumo·LATAM·Argentina·mid
pythondockerkubernetessparkairflowpytorchpandasnumpymlllmdata engineeringci/cd
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Azumo is currently looking for a highly motivated Data Scientist / Machine Learning Engineer to develop and enhance our data and analytics infrastructure. The position is FULLY REMOTE , based in Latin America. Professional English proficiency (B2/C1) This position will provide you with the opportunity to collaborate with a dynamic team and talented data scientists in the field of big data analytics and applied AI . If you have a passion for designing and implementing advanced machine learning and deep learning models, particularly in the Generative AI space, this role is perfect for you. We are seeking a skilled professional with expertise in Python for production-level projects, proficiency in machine learning and deep learning techniques such as CNNs and Transformers , and hands-on experience working with PyTorch . We’re looking for a versatile Machine Learning Engineer / Data Scientist to join our big-data analytics team. In this hybrid role you’ll not only design and prototype novel ML/DL models , but also productionize them end-to-end, integrating your solutions into our data pipelines and services. You’ll work closely with data engineers, software developers and product owners to ensure high-quality, scalable, maintainable systems. Key Responsibilities Model Development & Productionization Design, train, and validate supervised and unsupervised models (e.g., anomaly detection, classification, forecasting). Architect and implement deep learning solutions (CNNs, Transformers) with PyTorch . Develop and fine-tune Large Language Models (LLMs) and build LLM-driven applications. Implement Retrieval-Augmented Generation (RAG) pipelines and integrate with vector databases. Build robust pipelines to deploy models at scale ( Docker , Kubernetes , CI/CD ). Data Engineering & MLOps Ingest, clean and transform large datasets using libraries like pandas , NumPy , and Spark . Automate training and serving workflows with Airflow or similar orchestration tools. Monitor model p

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