ML, Engineer

Key Responsibilities

• Design, develop, train, optimize, and deploy machine learning models for real-world business use cases.
• Translate business and product requirements into scalable ML and AI solutions.
• Implement feature engineering, model selection, tuning, validation, and evaluation techniques.
• Develop and deploy ML models into production environments with high availability, scalability, and performance.
• Build and maintain machine learning pipelines including training, validation, deployment, and monitoring workflows.
• Monitor model performance, data drift, and model decay, and support retraining and optimization activities.
• Ensure ML solutions meet reliability, scalability, governance, and security standards.
• Collaborate with Data Scientists, Product Managers, Software Engineers, and Data Engineers across cross-functional teams.
• Support the development and maintenance of high-quality and reliable data pipelines.
• Participate in architecture discussions, design reviews, and code reviews following engineering best practices.
• Optimize models for latency, throughput, scalability, and operational cost efficiency.
• Implement experimentation and evaluation frameworks including A/B testing and offline evaluations.
• Apply Responsible AI principles including fairness, explainability, governance, and model transparency where applicable.