Machine Learning Engineer AWS & MLOps

Role Overview: As a Machine Learning Engineer, you will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud-native environment. Your role will involve working with a team of data scientists, engineers, and product teams to translate business requirements into effective ML solutions. Additionally, you will be implementing MLOps best practices to ensure the optimization of model performance and scalability in production environments. Key Responsibilities: - 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. Qualification Required: - 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. Additional Details of the Company: The company is seeking a highly skilled and motivated Machine Learning Engineer with a strong foundation in programming and machine learning, hands-on experience with AWS Machine Learning services (especially SageMaker), and a solid understanding of Data Engineering and MLOps practices. The role offers the opportunity to work in a collaborative environment with data scientists, engineers, and product teams to deliver innovative ML solutions.,