Sr. Machine Learning Engineer

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

We're looking for a Sr. Machine Learning Engineer

About the Role

We are looking for an experienced Senior Machine Learning Engineer to join our team and work on challenging, high-impact ML and AI systems. This is a hybrid position based in Montevideo, Uruguay.

You'll take ownership of the full ML lifecycle, from experimentation and model development to deployment and monitoring in production environments. If you're someone who thrives at the intersection of classical signal processing, deep learning, and modern LLM-powered systems, and you care about shipping things that actually work at scale, this role is for you.

What You'll Do

• Design, develop, and deploy machine learning models and AI-powered features into production systems used at scale.
• Build and maintain end-to-end ML pipelines — data ingestion, feature engineering, training, evaluation, and serving.
• Work with LLMs for solving real world problems, including prompt engineering, RAG architectures, LLM Observability and integration with orchestration frameworks like LangGraph, PydanticAI or OpenAI Agents.
• Collaborate with backend and data engineering teams to integrate ML solutions into existing infrastructure.
• Monitor and maintain models in production, ensuring reliability, performance, and fairness over time.
• Contribute to MLOps practices: experiment tracking, model versioning, CI/CD for ML, and observability.

What We're Looking For

• 4+ years of professional experience in Machine Learning or AI engineering roles.
• Strong foundations in ML — supervised/unsupervised learning, model evaluation, feature engineering.
• Hands-on experience building and shipping ML systems in production environments.
• Proficiency in Python and at least one deep learning framework (PyTorch preferred).
• Experience building and consuming APIs using FastAPI, Flask, or Django.
• Practical experience with LLMs — whether through fine-tuning, RAG, or building LLM-powered applications.
• Experience with cloud platforms and ML tooling, AWS preferred.
• Experience with containerization (Docker) and software engineering best practices (testing, versioning, CI/CD).
• Strong communication skills and advanced English proficiency (written and spoken) — you'll be working directly with English-speaking stakeholders.

Nice to Have

• Advanced knowledge of vector databases (Qdrant, Chroma, OpenSearch, pgvector) and semantic search.
• Background in NLP, information retrieval, or recommendation systems.
• Familiarity with model evaluation frameworks and responsible AI practices.
• Interest in keeping up with the latest ML and AI research.