MLOps experience with Google Vertex AI and production-grade ML platforms
Proficiency in ML frameworks (TensorFlow, PyTorch, scikit-learn) and containerization for integration
Hands-on experience with recommender systems, model deployment, AB testing, and data-driven optimization
Requirements:
Maintain expertise across ML technologies and platforms, prioritizing Google Vertex AI and integrating open-source frameworks via custom containers
Design and develop recommender systems using embedding-based retrieval, reinforcement learning, transformers, and LLMs; collaborate with teams to deploy in customer-facing products
Manage Vertex AI Feature Store for scalable feature sharing, security, and endpoint exposure
Collaborate on data labeling/management and ensure end-to-end data-to-AI integration using BigQuery/BigTable for ML modeling and BI tools
TOP SKILLS: MLOps and Google Vertex AI Job Description
Diverse ML Platform Expertise:
Maintain expertise in a range of ML technologies and platforms, with a preference for Google Vertex AI, but open to other systems as needed.
Leverage support for open-source frameworks like TensorFlow, PyTorch, scikit-learn, and integrate them with ML frameworks via custom containers.
Stay updated with the latest trends in MLOps and ML technologies.
Recommender System Design and Development:
Hands-on experience working on recommender systems, drawing from ML techniques such as embedding based retrieval, reinforcement learning, transformers, and LLMs.
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.
know more about current opportunities at LeadStack , please visit us on https://leadstackinc.com/careers/
Should you have any questions, feel free to call me on (513) 318-4502 or send an email on waseem.ahmad@leadstackinc.com
