Supports and performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment.

QUALIFICATIONS:

• Bachelor’s degree in a relevant field required; Master’s degree preferred.
• 6+ years of relevant experience in data engineering or machine learning engineering.
• Minimum 3 years of hands-on experience building ETL pipelines using AWS services
• Proven experience developing and implementing MLOps pipeline for deploying, monitoring, and managing AI/ML models in production.
• Proficient in Python and familiar with key machine learning frameworks and libraries.
• 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 Lifecycle (MDLC) and best practices.
• Experience designing and implementing end-to-end machine learning pipelines and solution architectures.

• Design, develop, and optimize complex data pipelines using machine learning engineering best practices to ensure scalability, efficiency, and reliability.
• Develop and implement robust MLOps pipeline to support the deployment, monitoring, and lifecycle management of AI/ML models in production environments.
• Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.
• Collaborate closely with data scientists to validate model-ready datasets and ensure thorough, accurate feature documentation.
• Conduct exploratory data analysis and discovery on raw data sources, incorporating business context to support model development.
• Track data lineage and perform root cause analysis during early-stage exploration or issue resolution.
• Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.
• Develop and maintain model monitoring scripts, investigate alerts, and coordinate timely resolutions.
• Act as a subject matter expert in machine learning engineering on cross-functional teams, contributing to high-impact initiatives.
• Stay current with advancements in AI/ML and evaluate their applicability to business challenges.