Machine Learning (AI) Engineer (Hybrid)

Role Overview

Machine Learning (AI) Engineer (Hybrid) to design, train, and deploy machine learning models that power core product features. You’ll collaborate with cross-functional teams to build scalable AI solutions and validate impact on accuracy and business outcomes.

Responsibilities

• Design, train, and optimize ML models using Python, TensorFlow, and PyTorch.
• Build and maintain data pipelines for high-quality training and evaluation data.
• Apply MLOps practices for deployment, monitoring, and model version control.
• Partner with data scientists and software engineers to integrate AI into production systems.
• Run performance benchmarking and A/B testing to measure model accuracy and business impact.

Required Skills & Qualifications

• Proficiency in Python, TensorFlow, and PyTorch.
• MLOps experience with tools such as MLflow, Kubeflow, or Amazon SageMaker.
• Strong foundation in data preprocessing, feature engineering, and model evaluation metrics.
• Familiarity with cloud platforms such as AWS, GCP, or Azure.
• Software engineering best practices, including Git and CI/CD pipelines.

Nice to Have

• Experience working in collaborative environments integrating ML capabilities into production at scale.

Compensation

• $200K – $500K per year (full-time).