Description

You will own the end-to-end lifecycle of machine learning models in production, from architecture and deployment to optimization and maintenance. Your focus is on building robust, scalable solutions within the healthcare domain using AWS and modern data engineering practices.

Responsibilities

• Implement and maintain machine learning models using CI/CD pipelines for seamless production deployment.
• Refactor code to meet enterprise software standards, ensuring rigorous error handling, logging, and performance optimization.
• Optimize compute resources to ensure low-latency model responses while minimizing AWS infrastructure costs.
• Troubleshoot and resolve deployment and performance issues in production environments to maintain system reliability.
• Ensure strict compliance with security and data privacy standards across all implementation activities.

Required Skills

• 11-13 years of total software engineering experience.
• 4+ years operationalizing machine learning models in production environments.
• 4+ years of experience working within Agile teams for design and delivery.
• 4+ years of solution architecting experience in cloud technologies, specifically AWS.
• 4+ years of hands-on experience with Machine Learning frameworks including Scikit-learn, PyTorch, and Spark ML.
• Strong proficiency in Python and PySpark for data processing and model development.
• 4+ years of experience with healthcare domain data and feature engineering for predictive models.

Preferred Skills

• Experience with Databricks, Gen AI, LLM implementation, and LLM orchestration.
• Proficiency in Spark/Scala and scripting languages like JavaScript, R, or Ruby.
• AWS solution architecture certification or data science/ML certifications from reputed institutions.

Key Skills

Education

Any Graduate