Key Facts

Remote From:

Anywhere

Full time

Senior (5-10 years)

English

Hard Skills

Other Skills

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Training And Development
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Collaboration
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Communication
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Teamwork
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Problem Solving

Roles & Responsibilities

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or related field (Ph.D. a plus)
3-5+ years of hands-on experience building machine learning models in production
Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch
Experience with ML pipeline tools (e.g., Airflow, Kubeflow, MLflow)

Requirements:

Design and implement machine learning models for classification, regression, recommendation, NLP, or time-series forecasting tasks
Develop, test, and maintain scalable ML pipelines for training, validation, and inference
Deploy models to production using MLOps practices with tools like MLflow, TFX, or SageMaker
Monitor and maintain the health of deployed models, updating them as needed

Job description

This is a remote position.

We are looking for a highly skilled Machine Learning Engineer to join our AI and data science team. In this role, you will design, develop, and deploy machine learning models and pipelines that power critical data-driven solutions across our organization. You’ll collaborate with data scientists, software engineers, and product teams to deliver intelligent systems at scale.

Responsibilities

Design and implement machine learning models for classification, regression, recommendation, NLP, or time-series forecasting tasks.
Develop, test, and maintain scalable ML pipelines for training, validation, and inference.
Collaborate with data engineers to build efficient data ingestion and feature extraction systems.
Optimize model performance using techniques like hyperparameter tuning, cross-validation, and regularization.
Deploy models to production using MLOps practices with tools like MLflow, TFX, or SageMaker.
Monitor and maintain the health of deployed models, updating them as needed.
Document ML experiments, metrics, and decisions.
Work closely with cross-functional teams to identify machine learning opportunities and define technical solutions.

Requirements

Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related field (Ph.D. a plus).
3–5+ years of hands-on experience building machine learning models in production.
Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
Experience with ML pipeline tools (e.g., Airflow, Kubeflow, MLflow).
Familiarity with cloud services (AWS, GCP, or Azure) and model deployment.
Solid understanding of statistics, data structures, and algorithms.
Experience with version control (Git), containerization (Docker), and CI/CD for ML.