MLOps Engineer with Google Vertex AI
Role: MLOps Engineer
Location: Remote (EST candidates preferred)
Job Type: Contract
Mandatory Skills:
Exp in MLOps, especially on Google Stack (Google Vertex AI and other cloud technologies).
Job Details:
• We are seeking a dynamic Senior Software Engineer with an ML focus to lead the integration and operationalization of machine learning models in our Search area.
• This role requires collaboration with data scientists and leadership teams, and a strong foundation in MLOps methodologies. Experience in diverse ML platforms, including Google Vertex AI and other cloud and open-source technologies, is essential.
• The candidate will bridge MLOps, data science, and leadership to ensure the smooth functioning of our ML infrastructure.
• Hands-on experience working on recommender systems, drawing from ML techniques such as embedding based retrieval, reinforcement learning, and transformers.
• Software engineering skills to work with teams integrating the recommender systems into customer facing products.
• Experience in AB testing and iterative optimization using data driven approaches.
• Understanding of infrastructure needs required to deploy ML systems (CPU/GPU, networking infrastructure).
Feature Store Management:
• Efficiently manage, share, and reuse machine learning features at scale using Vertex AI Feature Store.
• Implement feature stores as a central repository for maintaining transparency in ML operations across the organization.
• Enable feature delivery with endpoint exposure while maintaining authority and security features.
Data Management and Collaboration:
• Assist as needed with data labeling and management, ensuring high-quality data for ML models.
• Collaborate with data engineers and data scientists to ensure the integrity and efficiency of data used in ML models.
• Ensure end-to-end integration for data to AI, including the use of BigTable / BigQuery for executing machine learning models on business intelligence tools.
Continuous Monitoring and Optimization:
• Monitor ML systems in production, identify improvement opportunities, and implement optimizations.
• Participate in support rotations and participate in support calls as necessary.
Role: MLOps Engineer
Location: Remote (EST candidates preferred)
Job Type: Contract
Mandatory Skills:
Exp in MLOps, especially on Google Stack (Google Vertex AI and other cloud technologies).
Job Details:
• We are seeking a dynamic Senior Software Engineer with an ML focus to lead the integration and operationalization of machine learning models in our Search area.
• This role requires collaboration with data scientists and leadership teams, and a strong foundation in MLOps methodologies. Experience in diverse ML platforms, including Google Vertex AI and other cloud and open-source technologies, is essential.
• The candidate will bridge MLOps, data science, and leadership to ensure the smooth functioning of our ML infrastructure.
• Hands-on experience working on recommender systems, drawing from ML techniques such as embedding based retrieval, reinforcement learning, and transformers.
• Software engineering skills to work with teams integrating the recommender systems into customer facing products.
• Experience in AB testing and iterative optimization using data driven approaches.
• Understanding of infrastructure needs required to deploy ML systems (CPU/GPU, networking infrastructure).
Feature Store Management:
• Efficiently manage, share, and reuse machine learning features at scale using Vertex AI Feature Store.
• Implement feature stores as a central repository for maintaining transparency in ML operations across the organization.
• Enable feature delivery with endpoint exposure while maintaining authority and security features.
Data Management and Collaboration:
• Assist as needed with data labeling and management, ensuring high-quality data for ML models.
• Collaborate with data engineers and data scientists to ensure the integrity and efficiency of data used in ML models.
• Ensure end-to-end integration for data to AI, including the use of BigTable / BigQuery for executing machine learning models on business intelligence tools.
Continuous Monitoring and Optimization:
• Monitor ML systems in production, identify improvement opportunities, and implement optimizations.
• Participate in support rotations and participate in support calls as necessary.
