Machine Learning Engineer

Key Responsibilities:

Machine Learning Development:

• Design and implement ML models for predictive analytics, classification, regression, and clustering.
• Perform feature engineering, model tuning, and validation using frameworks like

  scikit-learn

  ,

  TensorFlow

  , or

  PyTorch

  .
• Deploy ML models into production environments and monitor performance.

Data Engineering & Processing:

• Build and optimize large-scale data pipelines using

  PySpark

  for distributed processing.
• Handle structured and unstructured data from multiple sources.
• Ensure data quality, consistency, and security across workflows.

Programming & Automation:

• Develop reusable scripts and automation tools in

  Python

  for data ingestion, transformation, and ML workflows.
• Implement best practices for code quality, testing, and version control (Git).

Collaboration & Integration:

• Work closely with data scientists, analysts, and business teams to translate requirements into technical solutions.
• Integrate ML models with APIs and applications for real-time predictions.

Required Skills:

• Strong proficiency in

  Python

  and ML libraries (

  scikit-learn

  ,

  TensorFlow

  ,

  PyTorch

  ).
• Hands-on experience with

  PySpark

  for big data processing.
• Solid understanding of ML concepts: supervised/unsupervised learning, model evaluation, hyperparameter tuning.
• Knowledge of SQL/NoSQL databases and data warehousing.
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices.