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