As an AI Backend Engineer (Python/Golang) at Intel, you will develop, test, and maintain backend services and APIs for Intel Geti, manage server environments, and optimize system performance. Your role will also involve using containerization with Docker and Kubernetes, implementing CI/CD pipelines, and collaborating with cross-functional teams to ensure the efficiency and reliability of the overall system.

To apply for the AI Backend Engineer (Python/Golang) position at Intel, you should possess at least a BS or MS in Computer Science or a related field, alongside 4-5 years of backend development experience with strong programming skills in Python or Golang. Familiarity with Docker, Kubernetes, and application design concepts is essential, along with good communication skills, as the tech team operates in English.

Intel fosters a collaborative and inclusive working environment for an AI Backend Engineer (Python/Golang). In this hybrid role, you can work both on-site and off-site while maintaining a flexible schedule. The workplace is designed to uphold the health and well-being of staff, and continuous professional development opportunities are available to expand your skills in both technical and soft areas.

For the AI Backend Engineer (Python/Golang) role at Intel, proficiency in either Python or Golang is crucial as this will be the primary focus of your backend development tasks. Strong programming skills are essential to effectively contribute to the development of the Intel Geti platform and to handle the associated backend services.

As an AI Backend Engineer (Python/Golang) at Intel, you will enjoy comprehensive rewards that include a stock program, annual bonuses, a robust employee pension plan, and a thorough medical plan complete with life insurance for you and your family. Additionally, you'll have access to various employee discounts, flexible working hours, and opportunities for career advancement through training and participation in innovative projects.

When approaching backend development in Python, it's essential to start with a clear understanding of the project requirements. I focus on designing scalable structures, writing clear and maintainable code, and utilizing frameworks and libraries effectively. Testing and continuous integration are also key components of my process.

I have extensive experience with Docker and Kubernetes, primarily focusing on containerizing applications for easy deployment and scaling. I use Docker to create uniform development and production environments and Kubernetes for orchestration, which simplifies managing and deploying applications across clusters.

CI/CD, which stands for Continuous Integration and Continuous Deployment, is a crucial practice in backend engineering. It involves automating the testing and deployment of code changes, ensuring that any new code integrates seamlessly with the existing codebase. This practice significantly reduces the risk of errors and downtime during deployments.

To optimize backend services, I focus on analyzing performance metrics, refining database queries, and implementing caching strategies to reduce load times. Additionally, I ensure that the codebase is modular, which allows for better scalability and maintenance.

When facing system failures or issues, I start by gathering performance data to identify the root cause. I then address the issue quickly to minimize downtime while implementing monitoring tools to prevent future occurrences. Post-issue, I analyze and document the problem to improve our response plans moving forward.

Communication is vital in collaborative development as it ensures alignment on project goals, clarifies responsibilities, and fosters teamwork. Regular updates via meetings or platforms help maintain transparency and encourage team members to voice concerns or suggestions, leading to a more cohesive workflow.

In backend development, I prioritize security by adopting practices such as input validation, proper authentication, and authorization measures. Regularly conducting code reviews, penetration testing, and keeping dependencies updated are also fundamental steps I implement to protect against vulnerabilities.

I have experience working with both SQL and NoSQL databases, which allows me to choose the appropriate type based on project requirements. For structured data with complex queries, I've utilized SQL databases effectively. In contrast, I prefer NoSQL for handling unstructured data that requires flexibility and scalability.

Backend development for AI applications often involves handling large datasets and ensuring efficient data processing. Balancing performance with the need for accurate model training is also challenging. Moreover, the integration of machine learning algorithms can complicate deployments, which requires careful architecture consideration.

I stay current with emerging backend technologies by engaging in continuous learning through online courses, webinars, and industry conferences. Additionally, I follow relevant blogs and participate in developer communities to exchange knowledge and gain insights into new tools and best practices.