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.

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.