Backend Engineer (Django & Next)

As a Backend Engineer (Django & Next) at Weekday's client, key responsibilities include developing and optimizing backend systems with Python, specifically using Django for AI-powered applications. You'll also enhance frontend interfaces using React/Next.js for seamless user experiences. Moreover, deploying cloud infrastructure on AWS or Azure, optimizing performance with services like Kubernetes and Docker, and collaborating with cross-functional teams are integral to this role.

To qualify for the Backend Engineer (Django & Next) position at Weekday's client, candidates should have at least three years of fullstack development experience focused on backend systems. Proficiency in Python and hands-on experience with Django or comparable frameworks is essential. Additionally, familiarity with React/Next.js for frontend development, cloud platform experience on AWS or Azure, and a strong understanding of containerization, preferably Kubernetes and Docker, are preferred.

A Backend Engineer (Django & Next) at Weekday's client can expect a fast-paced startup environment where innovation thrives. You will work directly with founders and leadership, engaging in high-impact projects that shape the future of AI technology. The culture encourages collaboration, problem-solving, and creativity, making it an exciting place for engineers who want to make a real difference.

The salary range for a Backend Engineer (Django & Next) at Weekday's client is between ₹35-70 LPA. This is negotiable based on your previous experience and skillset, which means your expertise could directly influence your compensation. It’s a competitive package that reflects the high-impact nature of the work you would be doing.

A Backend Engineer (Django & Next) at Weekday's client will primarily work with Python and Django for backend development, alongside React/Next.js for frontend. Familiarity with cloud services like AWS or Azure is also required, as is experience with containerization technologies, such as Kubernetes and Docker, which are incredibly beneficial for optimizing performance and scalability in applications.

In responding to this question, highlight specific projects where you successfully used Python and Django. Speak about your role in these projects, detailing aspects like building RESTful APIs, database management, or other functionalities you implemented. Mention any challenges you faced and how you overcame them, which showcases your problem-solving skills.

Discuss strategies you've implemented to optimize performance, such as code profiling, efficient database queries, and caching strategies. Additionally, talk about how you've used tools like Docker or Kubernetes for containerization and orchestration to ensure that your applications can scale efficiently under load.

When answering this question, provide examples of projects where you utilized React and Next.js. Explain your familiarity with concepts like component lifecycle, state management, and server-side rendering. Highlight any collaboration efforts with backend teams to create a seamless integration point as well.

Share a detailed account of your deployment experience. Describe the steps taken to prepare your application for the cloud, including pathfinding for cloud infrastructure, setting up services like EC2 or Azure App Service, and configuring networking aspects. Make sure to emphasize any security measures you considered in your approach.

Discuss the importance of communication and collaborative tools you've used, such as Jira or Slack, to coordinate with different teams. Provide an example where you successfully worked with product managers, designers, or other engineers to drive a project to completion, emphasizing how you navigated differences in priorities or expectations.

Define containerization in your answer and discuss how you've used it in your projects. Explain the benefits you’ve observed, such as environment consistency and simplified deployment processes, as well as any specific examples of how you’ve utilized Docker or Kubernetes in your work.

Share personal insights about the dynamic nature of startups, such as rapid pivots or resource constraints. Discuss how you’ve adapted to these challenges, perhaps by being proactive in learning new technologies or collaborating intensively with your team to ensure project success despite changing requirements.

Outline your debugging process, which could include using logging frameworks, debugging tools, or leveraging user feedback to trace issues. Discuss a few key instances where your debugging techniques resolved significant performance bottlenecks or bugs.

Talk about specific resources you utilize such as tech blogs, webinars, or community meetups to continuously learn and grow. Mention any certifications, courses, or personal projects that showcase your dedication to staying informed about new developments in backend technologies, particularly those relevant to AI and cloud computing.

Detail a specific project where you identified performance bottlenecks. Discuss the metrics you assessed and the tools you employed for profiling the application. Explain the optimizations you implemented and the positive outcome that resulted, whether it was improved user experience or resource savings.