Lead Software Engineer - AA

The Lead Software Engineer at Level AI is responsible for developing and executing the technical roadmap to scale our technology stack, designing and building highly scalable distributed systems, and driving engineering best practices throughout the software development lifecycle. Additionally, you will collaborate with diverse stakeholders to create and implement technical plans that align with business objectives.

To qualify for the Lead Software Engineer position at Level AI, candidates must possess a B.E/B.Tech/M.E/M.Tech/PhD from a recognized engineering institute, accompanied by over 5 years of backend and infrastructure experience. A strong background in large scale databases, high scale messaging systems, and real-time job queues is essential, along with experience in mentoring and leading engineering teams.

As a Lead Software Engineer at Level AI, familiarity with technologies such as Python, Django, PostgreSQL, Kafka, and tools for CI/CD is critical. Experience with large-scale distributed systems and real-time applications is also crucial. Bonus skills include Google Cloud, Redis, and Celery, particularly in the context of AI infrastructure operations.

At Level AI, professional growth is a key focus. Lead Software Engineers are encouraged to stay updated with the latest technologies and best practices, participate in mentorship programs, and engage in knowledge-sharing within the engineering community. Your role will include coaching junior engineers, ensuring you both contribute to and benefit from a culture of continuous learning.

A Lead Software Engineer at Level AI will work on a variety of projects aimed at enhancing customer engagement through AI-driven solutions. This could include designing scalable systems for real-time data processing, developing high-throughput messaging systems, and implementing operational improvements for critical services, all while collaborating closely with product management and stakeholders.

In your response, highlight specific projects where you designed or worked with distributed systems. Discuss the architectural patterns you utilized, challenges faced, and how you ensured scalability and reliability, emphasizing your problem-solving skills.

It's important to discuss your mentorship style, mentioning specific strategies you use, such as code reviews, pair programming, and providing constructive feedback. Focus on how you help junior engineers grow their skills and confidence.

Share your philosophy on software development practices. Discuss the importance of writing clean code, conducting thorough testing, and continuous integration/deployment, alongside any tools you prefer for ensuring quality.

This is an opportunity to showcase your analytical thinking and technical skills. Walk the interviewer through the problem, your thought process, the solution you implemented, and the outcome, emphasizing your results-oriented approach.

Communication is key. Discuss your strategies for effective collaboration, such as regular check-ins, aligning on goals, and using project management tools, emphasizing the importance of clear communication in delivering successful projects.

Talk about how you apply modern architectural principles like microservices, serverless computing, or event-driven architecture in your projects, explaining the benefits and challenges that come with each approach.

Demonstrate your understanding of Kafka by explaining its role in managing real-time data streams. Discuss specific use cases you've encountered, emphasizing how it fits into the overall architecture of scalable systems.

Define operational excellence in terms of high availability, performance, and reliability of systems. Discuss practices like monitoring, alerting, and incident response, and how you implement these in your work.

Focus on specific AI technologies you’ve worked with. Explain the context, your role in implementing them, and the impact they had on the project or overall customer experience.

Discuss pitfalls such as poor database design, security vulnerabilities, and lack of scalability. Share proactive strategies you use, such as code reviews, flexible architecture design, or technical debt management to avoid these issues.