Data Scientist / Machine Learning Engineer
- Role
- AI / ML
- Experience
- Mid
- Employment
- Contract
Open to Anywhere in LATAM · UTC-8…UTC-5. Set where you work from to check your eligibility.
No BS summary
Data Scientist / ML Engineer with 2+ years building, deploying, monitoring, and maintaining production ML models. Must be LATAM-based, aligned with US/Central business hours, strong in Python, SQL, Git, and enterprise ML platforms like AWS SageMaker, Vertex AI, or Azure ML. English-only interviews, documentation, and daily communication.
Core skills
Required skills
Optional skills
Required languages
📌 Position: Data Scientist - Machine Learning (Production & AI)
Location: Remote from LATAM
Contract Type: Full-time contractor (via In All Media)
Time Zone Alignment: US Time Zones (Central Time)
🧭 About In All Media
In All Media is a global technology and design firm focused on building impactful digital solutions through remote, distributed teams across LATAM. We partner with international clients across industries, providing long-term technical expertise, product innovation, and team augmentation.
🚀 Project Overview
The project centers on building, scaling, and maintaining end-to-end production machine learning pipelines and infrastructure in a cloud-native setting. The primary goal is to empower data-driven decision-making across the enterprise by delivering robust, scalable ML solutions. In this role, you will bridge model experimentation with production deployment, helping the team advance its core predictive algorithms while expanding into modern AI and LLM-driven capabilities.
🔍 Key Responsibilities
- ML Model Development & Deployment: Develop, deploy, and maintain production-grade Machine Learning (ML) models in cloud environments beyond local notebook development.
- Feature Engineering & EDA: Perform Exploratory Data Analysis (EDA) and design feature engineering pipelines to support production ML workflows.
- Pipeline Infrastructure: Build, maintain, and optimize data pipelines that feed and sustain ML models in production.
- Model Monitoring & Maintenance: Monitor model performance, health, and drift post-deployment to ensure continuous reliability in production.
- Cross-Functional Collaboration: Partner with software engineering, data, and business stakeholders to translate business goals into scalable ML solutions.
- AI & LLM Integration: Contribute to Artificial Intelligence (AI) and Large Language Model (LLM) based capabilities where applicable.
💡 Must-Have Skills
- Production ML Experience: 2+ years of professional experience building, deploying, monitoring, and maintaining production ML models in real-world environments.
- Programming & Data Languages: Strong Python programming skills along with advanced SQL capabilities.
- Enterprise ML Platforms: Experience utilizing AWS SageMaker or equivalent enterprise ML platforms (such as Google Vertex AI or Azure ML) to support production pipelines.
- Version Control & Autonomy: Proficient with Git for version control and a demonstrated ability to work independently within production settings.
🌟 Nice-to-Have Skills
- MLOps Ecosystem: Familiarity with MLOps and orchestration tools such as MLflow, Apache Airflow, or dbt (Data Build Tool).
- Data Warehousing: Hands-on experience working with Snowflake.
- Domain Expertise: Prior background in marketing, growth, experimentation, or causal inference.
- Generative AI: Practical experience working with LLMs (Large Language Models) or autonomous AI agents.
- Methodologies: Experience operating within Agile development environments.
🌐 Time Zone & Collaboration
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.
💬 Language
All interviews, documentation, and daily communication will be conducted exclusively in English.
Note: This contract is managed through In All Media. We provide the platform and infrastructure for LATAM’s top talent to work with global leaders.
What you'll do
- 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.
What they require
- 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.
- 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.
- Preferred: Familiarity with MLOps and orchestration tools such as MLflow, Apache Airflow, or dbt (Data Build Tool).
- Preferred: Hands-on experience working with Snowflake.
- Preferred: Prior background in marketing, growth, experimentation, or causal inference.
- Preferred: Practical experience working with LLMs (Large Language Models) or autonomous AI agents.
- Preferred: Experience operating within Agile development environments.
Inallmedia.com is a global technology and design firm focused on building impactful digital solutions through remote, distributed teams across LATAM. We partner with international clients across industries, providing long-term technical expertise, product innovation, and team augmentation.