Machine Learning Engineer

Machine Learning Engineer

Responsibilities

• Design, develop, and deploy end-to-end machine learning pipelines on 

  AWS SageMaker

  .
• Collaborate with data scientists to translate models and algorithms into production-ready code.
• Manage the entire machine learning lifecycle, including data ingestion, model training, validation, and deployment.
• Implement MLOps best practices to automate workflows and ensure model performance and reliability.
• Optimize ML models for performance, scalability, and cost efficiency on the AWS platform.
• Monitor and troubleshoot deployed models to ensure they meet performance and business requirements.
• Stay current with the latest advancements in machine learning and cloud technologies.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in a machine learning or data science role.
• Hands-on experience with 

  AWS services

  , specifically 

  SageMaker

  .
• Proficiency in Python and standard ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with containerization technologies like Docker.
• Solid understanding of MLOps principles and tools for CI/CD.
• Knowledge of data engineering principles and experience with big data technologies (e.g., Spark, Hadoop).
• Strong problem-solving skills and the ability to work in a collaborative environment.

Preferred Qualifications

• Experience with other AWS services such as 

  S3, Lambda, Glue, and Redshift

  .
• Certifications such as 

  AWS Certified Machine Learning – Specialty

  .
• Experience with model serving frameworks (e.g., Flask, FastAPI).
• Experience with version control systems like Git.
• Familiarity with agile development methodologies.