8 Machine Learning Engineer jobs in Argentina available for remote work from home. Apply for positions such as Senior Machine Learning Engineer, Machine Learning Engineer (Edge AI & Computer Vision), Machine Learning Engineer Sr. (IA Generativa) and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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You will own the architecture, development, and automation of end-to-end machine learning solutions while bridging the gap between data science and software engineering. Responsibilities include managing the ML lifecycle, ensuring cloud cost-efficiency, and collaborating with cross-functional teams to deliver scalable production models.
You will take end-to-end ownership of machine learning models, ensuring their reliable deployment and performance on physical hardware. Additionally, you will design and optimize real-time AI pipelines while collaborating with cross-functional teams and clients.
Design and execute applied research to validate the use of Machine Learning and Generative AI in real-world scenarios. Create Proofs of Concept (POCs) to reduce technical uncertainty for productive and commercial initiatives.
Design and maintain scalable data pipelines and architectures to power data warehouses, LLM-driven applications, and AI-based BI. Develop RAG workflows, manage prompt engineering, and implement natural language query experiences for analytics.
The role involves working on an intelligent matching project between doctors and pharmaceutical products, requiring technical evolution towards Generative AI and LLMs. Responsibilities include developing and maintaining data pipelines and applying classical Machine Learning techniques.
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The mission involves designing and executing applied research to validate the use of Machine Learning and Generative AI in real-world scenarios with prospects or clients. This is achieved by creating Proofs of Concept (POCs) to reduce technical uncertainty and establish foundations for productive and commercial initiatives.
The role involves building intelligent systems by working with data and transforming machine learning models into practical, real-world solutions. This includes developing, implementing, and deploying these ML models.
The mission involves designing and executing applied research to validate the use of Machine Learning and Generative AI in real-world scenarios with prospects or clients. This is achieved by creating Proofs of Concept (POCs) to reduce technical uncertainty and establish foundations for productive and commercial initiatives.
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