Job
Description
> Job Summary: We are 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 . You will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud-native environment. 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. 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.
