Machine Learning (Ops) Engineer

If you want to create a 21st-century database that uses the power of Cloud and AI and enables modern GenAI apps, come to Couchbase. We have a world-class engineering team that is working not only on bringing AI to Couchbase but is working on bringing Couchbase into AI. We have added enterprise-grade security, finely tuned LLMs, and have integrated that fully into a platform that we are very proud of - all we are missing is you!

As a Machine Learning (Ops) Engineer you will be responsible for developing and maintaining the infrastructure required to deploy, monitor, and manage machine learning models efficiently and effectively. This role is critical in bridging the gap between data science and engineering, ensuring that our AI solutions are scalable, reliable, and seamlessly integrated into our products. If you are someone who thrives in a fast-paced environment, is passionate about AI, and is constantly looking for ways to optimize and automate machine learning workflows, we'd like to talk to you.

This position is based in our Santa Clara, CA headquarters with expectations for the candidate to come into the office 2-3 days/week. We do not offer relocation assistance.

Responsibilities

• ML Model Deployment: Automate and streamline the deployment of machine learning models to production, ensuring they are scalable, secure, and efficient.
• Pipeline Development: Build and maintain robust CI/CD pipelines for machine learning systems, enabling rapid iteration and delivery of AI features.
• Performance Monitoring: Implement monitoring tools and processes to track the performance of machine learning models in production, identifying any issues or degradation over time.
• Collaboration: Work closely with data scientists and engineers to ensure machine learning models are effectively integrated into Couchbase's products and systems.
• Environment Management: Manage the ML development and production environments, ensuring they are optimized for performance and aligned with project requirements.
• Tooling and Automation: Identify, evaluate, and implement tools and technologies to improve the efficiency and effectiveness of the machine learning lifecycle.
• Best Practices: Advocate for and implement best practices in MLOps, ensuring high standards of quality, reliability, and performance across all AI initiatives.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, with a focus on machine learning, AI, or data science.
• Proven experience (5+ years) in MLOps, DevOps, or software engineering, with a strong understanding of machine learning models and algorithms.
• Proficiency in programming languages such as Python, and experience with machine learning frameworks (e.g., TensorFlow, PyTorch). Familiarity with database in general, Couchbase in particular is a plus.
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI) and containerization technologies (e.g., Docker, Kubernetes).
• Knowledge of cloud platforms (AWS, Azure, Google Cloud) and experience deploying scalable machine learning solutions in the cloud.
• Strong problem-solving skills and the ability to work collaboratively across teams.
• Excellent communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.