Description

Build and deploy scalable machine learning models to solve business problems, owning the full lifecycle from data exploration to production.

This role is on-site.

Responsibilities

• Build and deploy scalable ML models to address real-world business challenges.
• Own the complete ML lifecycle, including data exploration, feature engineering, prototyping, validation, and productionization.
• Optimize and automate workflows using MLOps principles and best practices.
• Collaborate cross-functionally with data scientists, engineers, and business stakeholders.
• Mentor team members and guide engineering best practices across projects.

Required Skills

• 5+ years of experience in machine learning engineering.
• Expert-level proficiency in Python and SQL.
• Extensive hands-on experience with AWS SageMaker (training, tuning, endpoints, pipelines).
• Strong understanding of modern ML toolkits such as scikit-learn and XGBoost.
• Proven ability to work with large, complex datasets and cloud-based infrastructure.
• Experience translating complex ML concepts into clear, actionable insights for business stakeholders.

Key Skills

Education

Any Graduate