Machine Learning Engineer ( Latin America)

About the job Machine Learning Engineer ( Latin America) Role: Machine Learning Engineer Location: Mexico / Uruguay / Colombia /Peru /Argentina /Chile /Costa Rica /Puerto Rico /Nicaragua /Dominican Republic /ElSalvador/Honduras/ Panama (Remote) Years of Experience: 4-5 years Pay: $48,000 - $55,000 PA Required Skill: Python, ML, AWS/GCP/Azure, Docker/Kubernetes. LLMs, GenAI, OOP, TensorFlow/PyTorch/Keras/scikit-learn Language Required: English C1 Level Requirements 4 years of ML experience at a start-up or larger enterprise high priority 6 months of experience with Large Language Models (LLMs) and Generative AI (GenAI) applications high priority Client delivery experience high priority Effective written and oral communications skills (C1/C2 advanced/proficient level English is required) high priority Bachelors degree in computer science, software engineering or related field Experience with cloud environments (e.g., AWS, Azure, GCP) Experience with ML frameworks and libraries (TensorFlow, PyTorch, Keras, scikit-learn) Experience developing, deploying, and managing/monitoring models Knowledge of containerization technologies (e.g., Docker, Kubernetes) and microservices architecture Expertise in Object-Oriented Programming (OOP) principles and unit test-driven development methodologies Advanced experience in NLP techniques and applications Proficiency in Python programming Familiarity with prompt engineering approaches and best practices Knowledge of data structures, data modeling, and software architecture Analytical and problem-solving skills, with the ability to propose innovative solutions and troubleshoot issues Ability to work independently and as part of a collaborative team in a fast-paced environment Experience in any of the following is preferred, not required : Agent development Data privacy Fine tuning LLMs LLM architecture and techniques for performance MLOps ML evaluation Model decay and data drift detection and handling Pulumi, Terraform and/or Cloud SDKs PySpark Quantization Retrieval-augmented generation (RAG) optimization Security Vector databases