Senior Backend Engineer, AI

As a Senior Backend Engineer, AI at Sentient, your key responsibilities include designing and implementing fault-tolerant backend infrastructure on AWS and GCP, architecting robust performance-critical services, and creating scalable AI workflows. You will also maintain REST and Websocket APIs, collaborate with cross-functional teams to ship new features, and ensure security measures are followed during deployment.

To qualify for the Senior Backend Engineer, AI position at Sentient, candidates should have over 5 years of hands-on experience in developing large applications, extensive expertise in Python, and familiarity with various AWS and GCP services. Knowledge of containerization with Docker and orchestration using Kubernetes is also critical, along with an understanding of Generative AI and problem-solving skills to navigate a fast-paced startup environment.

A Senior Backend Engineer, AI at Sentient should be familiar with a range of technologies including AWS (EC2, Lambda, RDS, etc.), GCP services (GCE, Cloud SQL, etc.), containerization technologies like Docker, and orchestration tools such as Kubernetes. Understanding Infrastructure as Code, and AI technologies including LLMs and fine-tuning datasets are also essential for this role.

At Sentient, collaboration is at the heart of our operations. We embrace teamwork among cross-functional groups, enabling engineers, designers, and AI researchers to work closely together. Our decentralized model encourages input from various community voices, fostering an open and engaging environment where collective intelligence drives innovation and improvement.

As a Senior Backend Engineer, AI at Sentient, you can expect competitive salary, equity options, and comprehensive insurance benefits. Our flexible PTO and work-from-home policy provide a healthy work-life balance, and you will have the chance to collaborate with top-tier engineers and cutting-edge technology, playing an essential role in shaping the future of AI.

During the interview, focus on detailing your practical experiences with specific services under AWS and GCP, such as EC2, Lambda, Cloud Functions, or their equivalent. Highlight projects where you utilized these services, emphasizing how you optimized performance or resolved challenges effectively. Use concrete examples to illustrate your hands-on experience.

Discuss particular challenges you've encountered with serverless solutions, such as cold starts, monitoring, or cost management. Formulate your answers around specific scenarios and how you addressed them, including lessons learned that you can apply to future projects in a senior capacity.

Talk about security best practices you've adopted, such as IAM policies, encryption methods, or compliance measures. Provide examples illustrating how you implemented these security features on AWS or GCP and how they contributed to overall system integrity and data protection.

When answering, explain your methodology for designing RESTful APIs, covering aspects like resource modeling, endpoint design, and security measures. Use past projects to illustrate how you enforced best practices and ensured smooth integration with front-end applications.

Detail the principles of microservices architecture and its benefits such as improved scalability, ease of deployment, and fault isolation. Provide examples from your experience where using microservices led to measurable improvements in a project.

Share a specific situation where you managed a critical production issue, focusing on your rapid response, root cause analysis, and the changed approach you employed afterward. Highlight the learning outcomes that led to enhanced reliability in your systems.

Emphasize the importance of documentation in maintaining transparency and reproducibility in development. Discuss how you incorporate documentation practices into your workflow and how it helps in onboarding new team members effectively.

Mention specific forums, conferences, or online courses where you stay updated with technology trends. Discuss your approach to learning and experimenting with new technologies to continuously innovate and improve your skills in the rapidly evolving AI landscape.

Outline key factors when designing for scalability, such as load balancing, microservices architecture, and data sharding. Provide examples from past projects where you implemented scalable solutions that allowed platforms to handle increased user loads.

Discuss your understanding of AI inference and the elements involved in building a successful inference pipeline. Include your thoughts on model deployment, scaling, and monitoring to ensure high performance and reliability of AI applications in a production environment.