Engineer Level 2

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 close collaboration with data scientists and leadership teams leveraging MLOps best practices to ensure smooth deployment and operation of ML models. The ideal candidate will have expertise in diverse ML platforms including Google Vertex AI cloud technologies and opensource solutions.

This role sits at the intersection of MLOps data science and software engineering ensuring the robustness and scalability of our ML infrastructure.

Required Qualifications:

• 5 years of experience in software engineering with a focus on machine learning and MLOps.
• Expertise in Google Vertex AI cloud ML platforms and opensource ML tools.
• Handson experience with recommender systems and deep learning frameworks.
• Strong software engineering skills to integrate ML models into largescale applications.
• Experience with A/B testing model evaluation and optimization techniques.
• Solid understanding of infrastructure needs for ML deployment (GPU/CPU networking scaling).
• Proficiency in Python TensorFlow PyTorch and distributed computing frameworks.
• Strong collaboration skills to work with data scientists engineers and leadership teams.

Key Responsibilities:

Recommender Systems & ML Model Development

• Develop and integrate recommender systems into customerfacing products.
• Implement ML techniques such as embeddingbased retrieval reinforcement learning and transformers.
• Collaborate with engineering teams to ensure seamless model integration.
• Drive A/B testing and iterative optimization using datadriven methodologies.
• Assess infrastructure needs for ML deployment including CPU/GPU resources and networking requirements.

Feature Store Management

• Efficiently manage share and reuse machine learning features at scale using Vertex AI Feature Store.
• Implement centralized feature stores to maintain transparency and consistency across ML operations.
• Enable secure and scalable feature delivery while maintaining access control and governance.

Data Management & Collaboration

• Work with data engineers and scientists to ensure highquality labeled datasets.
• Ensure endtoend integration of data pipelines to AI workflows using BigQuery and BigTable.
• Optimize data structures and storage to enhance model performance and efficiency.

Continuous Monitoring & Optimization

• Monitor ML systems in production to identify bottlenecks and improvement opportunities.
• Implement automation strategies to improve model retraining deployment and performance tracking.
• Participate in support rotations and troubleshoot production ML issues as needed.

Handson experience working on recommender systems drawing from ML techniques such as embeddingbased 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).