MLOps Engineer in Remote, USA

Hi,

 

We have urgent requirement of MLOps Engineer in Remote, USA 

 

Here are the job details

 

Job Title: MLOps Engineer

 

Location: Remote, USA  (Need to work in EST time zone)

 

Mandatory Skills: Exp. in MLOps, especially on Google Stack (Google Vertex AI and other cloud technologies).

 

 

Job Description:

•              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.

 

 

Regards,

 

Ajay Tanwar

Team Lead

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