ML Ops Engineer
ML Ops engineer
Remote
12+ months contract
Rate- $70/hr on C2C
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
Nitesh Jaiswal | Tekgence Inc
Linkedin:- linkedin.com/in/nitesh-ch-a378b5222
Direct: 469-421-5604 , Ext- 218
• nitesh.j@tekgence.com
6655 Deseo Dr, Suite 104,Irving, TX , 75039
• www.tekgence.com
ML Ops engineer
Remote
12+ months contract
Rate- $70/hr on C2C
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
Nitesh Jaiswal | Tekgence Inc
Linkedin:- linkedin.com/in/nitesh-ch-a378b5222
Direct: 469-421-5604 , Ext- 218
• nitesh.j@tekgence.com
6655 Deseo Dr, Suite 104,Irving, TX , 75039
• www.tekgence.com
