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