Machine Learning Engineer

ImpetusIT is seeking a Machine Learning Engineer for its media client. The role involves designing, developing, and deploying machine learning models while maintaining the data infrastructure that supports them.

Responsibilities:

• You’ll be responsible for designing, developing, and deploying machine learning models while also building and maintaining the data infrastructure that powers them
• Build and optimize data pipelines for training and inference workflows
• Develop, train, and deploy machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn
• Collaborate with data scientists, analysts, and product teams to define data requirements and model objectives
• Implement model monitoring, versioning, and retraining strategies
• Ensure data quality, lineage, and governance across ML pipelines
• Develop, train, and deploy ML models using frameworks like PyTorch , Scikit-learn , or XGBoost
• Leverage MLOps tools such as MLflow , GenAI Kubeflow , SageMaker , or Vertex AI for model lifecycle management
• Proficiency in Python and SQL; experience with Spark, Airflow, or similar tools
• Strong understanding of ML lifecycle, from data preprocessing to model deployment
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)

Requirements:

• Experience of 2-3 years
• Designing, developing, and deploying machine learning models
• Building and maintaining the data infrastructure that powers machine learning models
• Building and optimizing data pipelines for training and inference workflows
• Developing, training, and deploying machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn
• Collaborating with data scientists, analysts, and product teams to define data requirements and model objectives
• Implementing model monitoring, versioning, and retraining strategies
• Ensuring data quality, lineage, and governance across ML pipelines
• Developing, training, and deploying ML models using frameworks like PyTorch, Scikit-learn, or XGBoost
• Leveraging MLOps tools such as MLflow, GenAI, Kubeflow, SageMaker, or Vertex AI for model lifecycle management
• Proficiency in Python and SQL
• Experience with Spark, Airflow, or similar tools
• Strong understanding of ML lifecycle, from data preprocessing to model deployment
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)