Description

You will design, develop, and optimize complex data pipelines using ML engineering best practices.

Responsibilities

• Develop and implement MLOps pipelines for deploying, monitoring, and managing AI/ML models in production.
• Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.
• Collaborate with data scientists to validate model-ready datasets and ensure accurate feature documentation.
• Conduct exploratory data analysis on raw data sources, incorporating business context to support model development.
• Develop and maintain model monitoring scripts, investigating alerts and coordinating timely resolution.

Required Skills

• 7+ years of relevant experience in AI, ML, or Data Engineering.
• Minimum 3+ years building ETL pipelines using AWS services.
• Proven experience developing and implementing ML pipelines for production deployment and monitoring.
• Proficiency in Python and familiarity with key machine learning frameworks.
• Strong understanding of cloud technologies and AI/ML platforms like AWS SageMaker.
• Knowledge of the Machine Learning Development Life Cycle (MDLC) and AI engineering best practices.
• Experience designing and implementing end-to-end machine learning pipelines and solution architectures.
• Solid grasp of software engineering principles (design patterns, testing, security, version control).

Key Skills

Education

Any Graduate