Machine Learning Operations Engineer

JOB DESCRIPTION

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.

QUALIFICATIONS & EXPERIENCE

• 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.

ABOUT US

Buzz Solutions safeguards the world’s energy infrastructure by providing AI-based actionable insights and predictive analytics for power line and energy infrastructure inspections.
We are disrupting the power utility industry by providing automation for inspection and maintenance operations for infrastructure, using our AI-powered software platform, and are a venture-funded startup looking for great talent to share our vision of energy industry transformation.
Our solutions and technology allows the utilities to reduce current inspection operation costs by at least 50%, prevent power outages, prevent wildfires and manage their assets in an effective way.
You will be working with a talented, passionate and hardworking team of engineers and product/project managers to build end-to-end Machine Learning products and pipelines that will be deployed for our customers in order to prevent issues such as power outages, forced shutdowns, impact of climate change on the aging grid infrastructure and preventing wildfires

• 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.