Description

You will build, train, and optimize machine learning models for real-world applications, owning the full lifecycle from data pipelines to production deployment.

This role is on-site.

Responsibilities

• Develop data pipelines for collecting, cleaning, and preparing large datasets.
• Deploy ML models into production using APIs, containers, or cloud services.
• Monitor model performance, accuracy, and drift over time.
• Optimize models for performance, scalability, and cost.
• Collaborate with product, backend, and frontend teams to integrate ML into applications.

Required Skills

• Strong experience with Python and 3–8 years in machine learning or data-driven engineering.
• Hands-on experience with machine learning algorithms and model training.
• Experience with TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of data preprocessing, feature engineering, and evaluation metrics.
• Experience building and consuming REST APIs for ML services.
• Familiarity with SQL and data querying.

Preferred Skills

• Experience with cloud platforms such as AWS, GCP, or Azure.
• Knowledge of Docker and model deployment workflows.
• Experience with MLOps tools (MLflow, Airflow, or similar).

Key Skills

Education

Bachelor's degree