Description

You will lead the design, development, and optimization of machine learning systems.

This role is on-site.

Responsibilities

• Design and optimize complex data pipelines using ML engineering best practices for scalability and efficiency.
• 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 document features accurately.
• Develop and maintain model monitoring scripts, investigating alerts and coordinating timely resolution.

Required Skills

• 7+ years of relevant experience in AI engineering, ML engineering, or Data Engineering.
• 3+ years of hands-on experience building ETL pipelines using AWS services.
• Proficiency in Python and familiarity with key machine learning frameworks and libraries.
• Proven experience developing and implementing ML pipelines for production deployment and monitoring.
• Strong understanding of cloud technologies and AI/ML platforms like AWS SageMaker.
• Solid grasp of software engineering principles, including design patterns, testing, security, and 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

Bachelor's degree