We are seeking an experienced Data Science/Machine Learning Engineer with a proven track record of developing and deploying ML solutions at scale. The ideal candidate combines deep statistical knowledge with strong engineering capabilities, bringing 10+ years of experience in building production-ready AI/ML systems. This role bridges the gap between cutting-edge machine learning research and practical business applications, requiring both technical excellence and business acumen.

• Design and develop advanced machine learning solutions, from concept to production deployment, addressing complex business challenges.
• Build and optimize data pipelines, features, and ML infrastructure to support large-scale model training and inference.
• Lead the development of ML platforms and tools that enable efficient model development, deployment, and monitoring.
• Conduct sophisticated data analysis and create advanced statistical models for complex prediction and classification problems.
• Implement and maintain production ML systems with focus on scalability, reliability, and performance.
• Drive best practices for ML experimentation, version control, and reproducibility.
• Collaborate with domain experts to translate business requirements into technical solutions.
• Mentor data scientists and engineers on ML engineering best practices and system design.
• Establish frameworks for model performance monitoring, drift detection, and automated retraining.
• Research and implement new ML techniques to improve existing systems and solve novel problems.

• Design and develop advanced machine learning solutions, from concept to production deployment, addressing complex business challenges.
• Build and optimize data pipelines, features, and ML infrastructure to support large-scale model training and inference.
• Lead the development of ML platforms and tools that enable efficient model development, deployment, and monitoring.
• Conduct sophisticated data analysis and create advanced statistical models for complex prediction and classification problems.
• Implement and maintain production ML systems with focus on scalability, reliability, and performance.
• Drive best practices for ML experimentation, version control, and reproducibility.
• Collaborate with domain experts to translate business requirements into technical solutions.
• Mentor data scientists and engineers on ML engineering best practices and system design.
• Establish frameworks for model performance monitoring, drift detection, and automated retraining.
• Research and implement new ML techniques to improve existing systems and solve novel problems