Description

You own the engineering lifecycle for machine learning systems, from data pipeline design to production deployment and monitoring.

This role is on-site.

Responsibilities

• Design, develop, and optimize complex data pipelines using ML engineering best practices for scalability and efficiency.
• Develop and implement MLOps pipelines to manage the deployment, monitoring, and lifecycle of AI/ML models in production.
• Integrate and maintain data and model pipelines, diagnosing data quality issues and documenting assumptions.
• Collaborate with data scientists to validate model-ready datasets and ensure accurate feature documentation.
• Develop and maintain model monitoring scripts and investigate alerts to coordinate timely resolution.

Required Skills

• 7+ years of experience in AI, ML, or data engineering.
• Minimum 3+ years building ETL pipelines using AWS services.
• Proficiency in Python and familiarity with core ML frameworks.
• Experience implementing ML pipelines for production model deployment and management.
• Strong understanding of cloud technologies and platforms like AWS SageMaker.
• Solid grasp of software engineering principles (design patterns, testing, security, version control).
• 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.

Key Skills

Education

Any Graduate