Develop, deploy, and maintain production-grade Machine Learning (ML) models in cloud environments beyond local notebook development.
Perform Exploratory Data Analysis (EDA) and design feature engineering pipelines to support production ML workflows.
Build, maintain, and optimize data pipelines that feed and sustain ML models in production.
Monitor model performance, health, and drift post-deployment to ensure continuous reliability in production.
Partner with software engineering, data, and business stakeholders to translate business goals into scalable ML solutions.
Contribute to Artificial Intelligence (AI) and Large Language Model (LLM) based capabilities where applicable.

2+ years of professional experience building, deploying, monitoring, and maintaining production ML models in real-world environments.
Strong Python programming skills along with advanced SQL capabilities.
Experience utilizing AWS SageMaker or equivalent enterprise ML platforms (such as Google Vertex AI or Azure ML) to support production pipelines.
Proficient with Git for version control and a demonstrated ability to work independently within production settings.

Familiarity with MLOps and orchestration tools such as MLflow, Apache Airflow, or dbt (Data Build Tool).
Hands-on experience working with Snowflake.
Prior background in marketing, growth, experimentation, or causal inference.
Practical experience working with LLMs (Large Language Models) or autonomous AI agents.
Experience operating within Agile development environments.

The role requires alignment with US Time Zones. Candidates must be available during standard US business hours to ensure real-time collaboration with cross-functional engineering, data, and business partners.
All interviews, documentation, and daily communication will be conducted exclusively in English.
This contract is managed through In All Media. We provide the platform and infrastructure for LATAM’s top talent to work with global leaders.

InAllMedia is hiring a Data Scientist / Machine Learning Engineer