Senior ML Engineer - Production ML & LLM Systems

Pento; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Pento in Montevideo, Uruguay seeks a Senior Machine Learning Engineer to own the ML lifecycle from experimentation through deployment and monitoring. You will build and deploy ML models and AI-powered features at scale, and collaborate with backend and data teams to ensure production readiness.

The role involves working with LLM-powered systems, prompt engineering, and MLOps practices, including experiment tracking, versioning, and CI/CD, in a hybrid work model with strong English communication

Qualifications

• 4+ years of professional ML/AI engineering experience.
• Strong ML foundations: supervised/unsupervised learning and feature engineering.
• Hands-on in production ML systems.
• Proficiency in Python and PyTorch.
• Experience building APIs with FastAPI, Flask, or Django.
• Experience with cloud platforms, AWS preferred.
• Containerization with Docker and CI/CD.
• ,
• Advanced English communication skills.

Responsibilities

• Design, develop, and deploy ML models and features into production systems used at scale.
• Build end-to-end ML pipelines — data ingestion, feature engineering, training, evaluation, and serving.
• Work with LLMs for prompts, RAG, observability, and integration with orchestration frameworks.
• Collaborate with backend and data engineering teams to integrate ML solutions into existing infrastructure.
• Monitor and maintain models in production for reliability, performance, and fairness.
• Contribute to MLOps: experiment tracking, model versioning, CI/CD, and observability.

Skills

Machine Learning

Python

Deep Learning

API Development

MLOps

English Proficiency

Tools

Docker

PyTorch

FastAPI

Flask

Django

AWS

Job description

Pento in Montevideo, Uruguay seeks a Senior Machine Learning Engineer to own the ML lifecycle from experimentation through deployment and monitoring. You will build and deploy ML models and AI-powered features at scale, and collaborate with backend and data teams to ensure production readiness.

The role involves working with LLM-powered systems, prompt engineering, and MLOps practices, including experiment tracking, versioning, and CI/CD, in a hybrid work model with strong English communication