Experienced Backend Developer (MLOps)

Chips Talk, We Listen 

proteanTecs is a game-changing startup that's giving advanced electronics the power to report on their own health. In a digital world built for autonomous driving, cloud computing, and AI, we depend on computing systems daily. But how can we guarantee their safety, reliability and functionality? proteanTecs is the first-ever company to provide visibility into next-gen chips while they are operating, based on the power of on-chip monitoring, machine learning, and data analytics. 

Here at proteanTecs, you'll be part of a team that's unlocking deep insights to make electronics more reliable, efficient, and high-quality. We're trusted by industry leaders in data centers, automotive, communications, and consumer devices – we work with the world's largest and most notable companies in tech. 

Why proteanTecs is a great place to work: 

• Fast-paced and impactful: We're a mission-driven startup, so you'll tackle new challenges daily, wear many hats, and see your work directly influence the future of electronics. 
• Supportive company culture: Learn from the best. Our 200+ team members are experts in their field with a proven track record of success, and they're committed to fostering a collaborative and supportive work environment. 
• International presence: We're a multinational company with a diverse team across multiple locations around the globe. You'll collaborate on projects with international impact, gaining a global perspective of the tech industry. 
• Work with industry leaders: Our solutions are used by the biggest names in tech. You'll be part of the team creating the next generation of groundbreaking products. 
• Cutting-edge playground: We use the latest machine learning, platforms, and tools to push boundaries and achieve breakthroughs. 
• Real-world impact: Our work keeps data centers, cars, and other critical systems running smoothly. Your work will directly contribute to safer, more reliable electronics. 
• We are here for the win: Backed by industry veterans and leading investors, we offer a stable and secure work environment with plenty of room for growth. 

Our team's perspective:

• Linor Harnik, Software Engineer - “The atmosphere here is great. Everyone's super welcoming, even the dogs! I’m happy to be part of something that's making a big difference in the industry.” 
• Yael Badian, Algorithms Engineer - "The company and I have been growing together for the past 6 years, and my role is becoming more influential as I gain more responsibilities within the team and the company". 
• Muhamed Eid, Logic Design Engineer - "The company believes in the team and respects them, providing us with multiple opportunities to challenge ourselves and grow, we are never bored here" 
• Guy Gozlan, Director of Machine Learning - "We create a never before seen solution that dramatically impacts one of the most important industries of our time" 

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.  

#LI-Hybrid

#Haifa/TLV

• 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

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