MLOps Engineer

Job Title: MLOps Engineer

Location: Remote

Duration: 6 Month(s), Contract to Hire

Preferred Skills:
• Experience working collaboratively with data science teams, understanding their needs and challenges.
• Ability to lead initiatives and communicate effectively with technical teams and senior leadership.
• Proven ability to understand company business problems and identify probable technical solutions to those problems.
• Familiarity with a range of ML tools and frameworks, and openness to adapting to emerging technologies. (ML Ops skills in the cloud)

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.

Key Responsibilities

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.