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The Machine Learning Operations Engineer develops backend pipelines for deploying ML models, builds Docker containers, manages Kubernetes on GCP, and integrates APIs. Responsibilities include performance monitoring, documentation, and collaboration with software engineers.
Join Buzz Solutions and be part of a dynamic team that is shaping the future of energy and technology. If you are passionate about delivering exceptional customer support and thrive in a collaborative and innovative environment, we want to hear from you! Apply now to embark on an exciting journey with us.
Responsibilities
- Develop end-to-end backend pipelines for deploying machine learning models.
- Build Docker containers for trained machine learning models.
- Deploy Docker container images on Kubernetes engine.
- Maintain Kubernetes engine and virtual machines on Google Cloud Platform.
- Develop Flask and FastAPI frameworks in Python for deploying models as APIs.
- Deploy the models in production as REST APIs on cloud infrastructure.
- Observe and measure current product performance.
- Review the process and product performance data with the team to develop standard work.
- Document the process, code reviews and workflow to streamline product enhancements.
- Develop end-to-end pipeline for deploying software platform backend infrastructure.
- Build end-to-end methodology for backend modules of the cloud software platform.
- Choose the optimal technology stack for building out the elements of the software platform backend.
- Establish platform features and timelines for product roadmap.
- Integrate SQL database objects with the software platform.
- Integrate machine learning model REST API endpoints with software platform.
- Deploy the platform in production on cloud infrastructure.
- Work with a team of software engineers to enhance performance of the software platform and run continuous unit tests for deployed products.
- Observe and measure current product performance.
- Review the process and product performance data w/ team to develop standard work.
- Document the process, code reviews and workflow to streamline product enhancements.
- Maintain and monitor cloud infrastructure.
- Monitor the logs of customer usage of the products and test for any vulnerabilities.
- Maintain database systems and optimize for performance and costs.
- Provide unit and stress testing frameworks for cloud infrastructure services deployed in production environments.
- Document the process, code reviews and workflow to streamline product enhancements.
Qualifications
- Must have a Bachelor’s degree in Computer Science or related field and 4 years of experience, including:
- Designing, implementing, debugging web technologies and server architectures (Java,JavaScript, NodeJS), databases in Cloud Infrastructure and with Python programming language
- Develop applications, API integrations on cloud infrastructures to handle customer data
- Utilizing and maintaining cloud infrastructure and services of Google Cloud Platform
- Employer will accept a Master’s degree and 2 years’ experience in lieu of the Bachelor’s plus 4.
Top Skills
Docker
Fastapi
Flask
Google Cloud Platform
Java
JavaScript
Kubernetes
Node.js
Python
SQL
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