Senior Machine Learning Engineer

As a Machine Learning Engineer at our company, you will play a crucial role in designing, developing, deploying, and maintaining production-grade machine learning systems. Your responsibilities will include: - Designing and optimizing scalable data pipelines for training, inference, and feature engineering. - Developing and managing feature stores to facilitate efficient model development and deployment. - Enhancing model performance, inference efficiency, and optimizing infrastructure costs. - Creating REST APIs for ML services utilizing FastAPI or similar frameworks. - Writing clean, testable, and maintainable Python code with comprehensive unit and integration tests using pytest. - Monitoring production ML systems, establishing and maintaining Service Level Objectives (SLOs), and engaging in incident response and root cause analysis. - Collaborating with Data Scientists, Data Engineers, and Product teams to convert business requirements into production-ready ML solutions. - Taking ownership of end-to-end delivery of ML projects from design through deployment and providing production support. - Mentoring junior engineers, conducting code reviews, and promoting engineering best practices. To excel in this role, you should possess: - 6-9 years of experience in Machine Learning Engineering or Software Engineering with a focus on ML. - Proficiency in Python, including pandas, NumPy, and scikit-learn. - Hands-on experience in building and deploying production-grade ML systems. - Familiarity with feature stores, ML infrastructure, and data pipeline design. - Expertise in developing APIs using FastAPI or similar frameworks. - Skills in model performance tuning, optimization, and cost-efficient deployment. - Experience with testing frameworks like pytest. - Knowledge of monitoring, observability, SLOs, and incident management for production systems. - Strong problem-solving, communication, and leadership abilities. Preferred qualifications include experience with cloud platforms (AWS, Azure, or GCP), familiarity with containerization and orchestration tools like Docker and Kubernetes, exposure to MLOps tools and CI/CD pipelines, and knowledge of distributed data processing frameworks such as Spark. Join us in this exciting journey of innovation and growth in the field of Machine Learning Engineering. As a Machine Learning Engineer at our company, you will play a crucial role in designing, developing, deploying, and maintaining production-grade machine learning systems. Your responsibilities will include: - Designing and optimizing scalable data pipelines for training, inference, and feature engineering. - Developing and managing feature stores to facilitate efficient model development and deployment. - Enhancing model performance, inference efficiency, and optimizing infrastructure costs. - Creating REST APIs for ML services utilizing FastAPI or similar frameworks. - Writing clean, testable, and maintainable Python code with comprehensive unit and integration tests using pytest. - Monitoring production ML systems, establishing and maintaining Service Level Objectives (SLOs), and engaging in incident response and root cause analysis. - Collaborating with Data Scientists, Data Engineers, and Product teams to convert business requirements into production-ready ML solutions. - Taking ownership of end-to-end delivery of ML projects from design through deployment and providing production support. - Mentoring junior engineers, conducting code reviews, and promoting engineering best practices. To excel in this role, you should possess: - 6-9 years of experience in Machine Learning Engineering or Software Engineering with a focus on ML. - Proficiency in Python, including pandas, NumPy, and scikit-learn. - Hands-on experience in building and deploying production-grade ML systems. - Familiarity with feature stores, ML infrastructure, and data pipeline design. - Expertise in developing APIs using FastAPI or similar frameworks. - Skills in model performance tuning, optimization, and cost-efficient deployment. - Experience with testing frameworks like pytest. - Knowledge of monitoring, observability, SLOs, and incident management for production systems. - Strong problem-solving, communication, and leadership abilities. Preferred qualifications include experience with cloud platforms (AWS, Azure, or GCP), familiarity with containerization and orchestration tools like Docker and Kubernetes, exposure to MLOps tools and CI/CD pipelines, and knowledge of distributed data processing frameworks such as Spark. Join us in this exciting journey of innovation and growth in the field of Machine Learning Engineering.