MLOps Engineer

Title: MLOps Engineer

Location: Cincinnati, OH (Remote Role)

Duration: 6+ Months CTH

Pay Rate: $80/HR - $90/HR

Job Description:

We are seeking a dynamic Senior Software Engineer with an Client 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 Client 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 Client infrastructure.

Key Responsibilities:

Diverse Client Platform Expertise:

• Maintain expertise in a range of Client 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 Client frameworks via custom containers.

• Stay updated with the latest trends in MLOps and Client technologies.

Recommender System Design and Development:

• Hands-on experience working on recommender systems, drawing from Client 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 Client 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 Client 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 Client models.

• Collaborate with data engineers and data scientists to ensure the integrity and efficiency of data used in Client 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 Client systems in production, identify improvement opportunities, and implement optimizations.

• Participate in support rotations and participate in support calls as necessary.