Machine Learning Engineer

Position Summary

Our client is seeking a talented Machine Learning Engineer to join their team and help build, deploy, and maintain machine learning systems at scale. The ideal candidate will bridge the gap between data science research and production systems, transforming ML models into robust, scalable solutions that deliver real business value.

Key Responsibilities:

• Design, develop, and implement machine learning models and algorithms to solve complex business problems
• Collaborate with data scientists to productionize research models and improve their performance
• Conduct experiments, A/B tests, and statistical analysis to validate model effectiveness
• Optimize models for performance, scalability, and resource efficiency
• Build and maintain ML pipelines, data processing workflows, and model serving infrastructure
• Implement MLOps best practices including model versioning, monitoring, and automated retraining
• Deploy models to production environments using containerization and cloud platforms
• Monitor model performance in production and implement alerting systems for model drift
• Develop data pipelines for feature engineering, data validation, and model training
• Work with large-scale distributed systems and big data technologies
• Ensure data quality, consistency, and availability for ML workflows
• Implement data governance and privacy practices
• Partner with cross-functional teams including product managers, software engineers, and business stakeholders
• Translate business requirements into technical ML solutions
• Mentor junior team members and contribute to best practices documentation
• Stay current with latest ML research and industry trends

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related field
• 3+ years of experience in machine learning engineering or related roles
• Proven track record of deploying ML models to production environments

Technical Skills Required:

• Proficiency in Python and/or R, with experience in ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost)
• Strong understanding of machine learning algorithms, statistics, and mathematical foundations
• Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes)
• Knowledge of data engineering tools and frameworks (Apache Spark, Kafka, Airflow)
• Proficiency in SQL and experience with both relational and NoSQL databases
• Experience with version control systems (Git) and CI/CD practices