Machine Learning Engineer AWS & MLOps

As a Machine Learning Engineer at our company, you will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud-native environment. - Design and implement machine learning models and pipelines using AWS SageMaker and related services. - Develop and maintain robust data pipelines for training and inference workflows. - Collaborate with data scientists, engineers, and product teams to translate business requirements into ML solutions. - Implement MLOps best practices including CI/CD for ML, model versioning, monitoring, and retraining strategies. - Optimize model performance and ensure scalability and reliability in production environments. - Monitor deployed models for drift, performance degradation, and anomalies. - Document processes, architectures, and workflows for reproducibility and compliance. Required Skills & Qualifications: - Strong programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch). - Solid understanding of machine learning algorithms, model evaluation, and tuning. - Hands-on experience with AWS ML services, especially SageMaker, S3, Lambda, Step Functions, and CloudWatch. - Experience with data engineering tools (e.g., Apache Airflow, Spark, Glue) and workflow orchestration. - Proficiency in MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines, Docker, Kubernetes). - Familiarity with monitoring tools and logging frameworks for ML systems. - Excellent problem-solving and communication skills. Preferred Qualifications: - AWS Certification (e.g., AWS Certified Machine Learning Specialty). - Experience with real-time inference and streaming data. - Knowledge of data governance, security, and compliance in ML systems. As a Machine Learning Engineer at our company, you will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud-native environment. - Design and implement machine learning models and pipelines using AWS SageMaker and related services. - Develop and maintain robust data pipelines for training and inference workflows. - Collaborate with data scientists, engineers, and product teams to translate business requirements into ML solutions. - Implement MLOps best practices including CI/CD for ML, model versioning, monitoring, and retraining strategies. - Optimize model performance and ensure scalability and reliability in production environments. - Monitor deployed models for drift, performance degradation, and anomalies. - Document processes, architectures, and workflows for reproducibility and compliance. Required Skills & Qualifications: - Strong programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch). - Solid understanding of machine learning algorithms, model evaluation, and tuning. - Hands-on experience with AWS ML services, especially SageMaker, S3, Lambda, Step Functions, and CloudWatch. - Experience with data engineering tools (e.g., Apache Airflow, Spark, Glue) and workflow orchestration. - Proficiency in MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines, Docker, Kubernetes). - Familiarity with monitoring tools and logging frameworks for ML systems. - Excellent problem-solving and communication skills. Preferred Qualifications: - AWS Certification (e.g., AWS Certified Machine Learning Specialty). - Experience with real-time inference and streaming data. - Knowledge of data governance, security, and compliance in ML systems.