Machine Learning Engineer

Responsibilities

• Design and implement scalable machine learning models from data preprocessing to deployment.
• Lead feature engineering and model optimization to improve performance and accuracy.
• Build and manage end-to-end ML pipelines using MLOps practices.
• Deploy, monitor, and maintain models in production environments.
• Collaborate with data scientists and product teams to understand business requirements and translate them into ML solutions.
• Conduct advanced data analysis and build visualization dashboards to support insights.
• Maintain thorough documentation of models, experiments, and workflows.
• Mentor junior team members on best practices and technical skills.

Skills

Must-have

• 2–3 years of experience in machine learning development, focusing on end-to-end model lifecycle.
• Proficiency in Python with Pandas, NumPy, and Scikit-learn for advanced data handling and feature engineering.
• Strong hands-on expertise in TensorFlow or PyTorch for deep learning model development.

Good-to-have

• Experience with MLOps tools such as MLflow or Kubeflow for model management and deployment.
• Familiarity with big data frameworks like Spark or Dask.
• Exposure to cloud ML services such as AWS SageMaker or GCP AI Platform.

Will be a plus

• Working knowledge of Weights & Biases and DVC for experiment tracking and versioning.
• Experience using Ray or BentoML for distributed training and model serving.