Description

Own the end-to-end lifecycle of machine learning models, balancing heavy engineering with operational stability. Focus on real-time data arbitration and email processing pipelines using Random Forest and transitioning to Transformer architectures.

Responsibilities

• Develop and maintain machine learning models for real-time data processing and arbitration.
• Refactor existing Random Forest implementations into modern Transformer-based architectures.
• Build and manage robust CI/CD pipelines for model deployment and updates.
• Operate and maintain infrastructure on AWS using Kubernetes.

Required Skills

• 5+ years of professional experience as a Machine Learning Engineer.
• Strong proficiency in Python is mandatory.
• Strong proficiency in Scala is mandatory.
• Hands-on experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn, and Keras.
• Production experience with AWS services.
• Experience deploying and managing containers with Kubernetes.
• Proven track record implementing CI/CD workflows for ML systems.

Preferred Skills

• Experience with real-time streaming data processing.
• Background in NLP or transformer model optimization.

Key Skills

Education

Any Graduate