Backend Developer (MLOps)

The Company:

proteanTecs is a dynamic fast-paced start-up company, transforming the way reliability of electronics is achieved. In a world where machines are gaining immense responsibility over our lives, sudden failure is not an option.

We have developed a cloud-based platform, which combines data created in chip-embedded Agents (IPs), with machine learning, to predict faults before they become failures. Our solutions provide unprecedented insights throughout the value chain, from Chip Vendors to System Vendors and Digital Service Providers.

The company was founded by seasoned industry veterans, including three former founders of Mellanox Technologies, with deep knowledge and experience in the electronics industry and is backed by worldwide leading investors.

At proteanTecs we combine different disciplines to create a revolutionary solution. Our team is highly interdisciplinary, ranging from SaaS and Machine Learning experts, to top designers in Chips and Circuits, as well as masters of EDA. We work together and learn from each other.

The Team:

Part of the machine-learning and Algorithm group, the MLOps team serves as a bridge between machine-learning and Software within the organization.

The team of 4 highly qualified engineers with both Software abilities and understanding of machine learning are responsible for streamlining the machine learning lifecycle, in charge of the machine learning infra structure, architecture and development of major common machine-learning components in production.

Requirements

• B.Sc. in Mathematics/Statistics/Physics/Computer Science/Electrical Engineering or related field
• 5+ years of experience in a similar engineering role
• Strong background in software engineering with experience in developing and deploying production-level software applications. – python advantage.
• Proficiency in machine learning concepts, algorithms, and tools to understand the requirements of data scientists and machine learning models.
• Experience in cloud computing platforms such as AWS, Azure, or Google Cloud Platform for deploying and managing machine learning infrastructure.
• Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes for scalable and efficient deployment of machine learning models.
• Familiarity with version control systems like Git for tracking changes in code and models.
• Understanding of continuous integration and continuous deployment (CI/CD) pipelines to automate testing and deployment processes.
• Strong problem-solving skills and the ability to troubleshoot issues related to machine learning infrastructure and deployments.
• Excellent communication skills to collaborate effectively with cross-functional teams including data scientists, software engineers, and stakeholders.
• Ability to both work as part of a team and independently, taking on projects, seeing them through every step, from initial design and testing to the final production phase.
• Knowledge in MLOps infra structure – MLRun advantage.
• Knowledge in Data engineering, parquet processing and pandas – advantage

Responsibilities

As an MLOps engineer, your responsibilities typically revolve around ensuring the smooth deployment, scaling, and maintenance of machine learning models within an organization. Some key responsibilities of an MLOps engineer include:

• Collaborating with data scientists and software engineers to understand the requirements for deploying machine learning models in production environments.
• Designing, building and maintaining infrastructure for machine learning training and inference in production, including cloud-based solutions and containerized environments.
• Monitoring the performance of deployed models, tracking key metrics, and troubleshooting issues to ensure optimal performance.
• Optimizing and scaling machine learning workflows to handle increasing data volumes and model complexities.
• Implementing infrastructure as code practices to manage machine learning infrastructure effectively.
• Staying updated on the latest trends and technologies in MLOps and continuously improving processes for efficiency and reliability.
• Developing and implementing CI/CD pipelines to automate testing, integration, and deployment of machine learning models.
• Responsibility for Machine Learning pipelines Quality