Machine Learning Engineer – AWS & MLOps

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.