AI Backend Engineer

As an AI Backend Engineer at our San Francisco-based company, your key responsibilities include designing, building, and optimizing high-performance GraphQL APIs to facilitate AI-powered search and retrieval systems. You'll also develop and maintain frameworks to orchestrate AI agents autonomously. Expect to spend around 70% of your time coding in Golang and Python, improving the architecture of GraphQL queries and performance while collaborating with ML/AI engineers.

To qualify for the AI Backend Engineer role at our company, candidates should have a minimum of 5 years of backend development experience, with a strong proficiency in Golang and Python. Familiarity with designing and optimizing GraphQL APIs for high-performance applications is essential, along with a solid understanding of AI retrieval systems and cloud experience, particularly with Azure.

AI Backend Engineers at our San Francisco office leverage an impressive tech stack including Golang, Python, GraphQL, PostgreSQL, Redis, Kubernetes, and cloud services on Azure. They also work extensively with vector databases such as Weaviate and Pinecone, and graph databases such as Neo4j and AWS Neptune, to implement advanced AI retrieval and recommendation systems.

The AI Backend Engineer will work in a collaborative environment, partnering closely with the Head of Engineering, ML/AI engineers, and other team members. This role encourages open communication and teamwork to solve complex problems in developing scalable AI-powered APIs, making the workplace extremely dynamic and engaging.

Bonus skills that would make you stand out as an AI Backend Engineer include experience with GraphQL federation, subscriptions via WebSockets, and familiarity with MLOps frameworks. Knowledge of real-time data updates and distributed system design patterns will also enhance your ability to contribute effectively to our AI-driven projects.

Discuss your experience by mentioning specific projects where you designed APIs that interact with AI components. Highlight your role in defining the architecture and approach to ensure efficiency and scalability, as well as your experience with GraphQL.

Optimizing GraphQL API performance requires strategies such as query batching, pagination, and caching. Be prepared to describe your approach to identifying slow queries and how you've used tools like DataLoader to ensure efficient data retrieval without over-fetching.

Share specific measures you've implemented in API security, such as OAuth, JWT token-based authentication, and role-based access controls. Discuss how these practices contribute to the safety of data and user interactions within the API environment.

Demonstrate your understanding of vector databases, explaining their role in AI for tasks such as similarity search based on high-dimensional vectors. Discuss experiences you've had with databases like Weaviate or Pinecone, and examples of how you've utilized them in AI-powered projects.

Collaboration is vital in AI projects. You could talk about established communication channels, regular meetings, or how you ensure a shared understanding of requirements between backend development and AI teams. Highlight any tools you've used to facilitate this collaboration.

Share your dedication to coding best practices by discussing your experience with writing clean, maintainable code, the importance of code reviews, adherence to coding standards, and practices such as test-driven development that contribute to overall code quality.

Demonstrate your understanding of scalability by mentioning techniques like microservices architecture, load balancing, and database optimization strategies. Provide examples where you've successfully scaled systems to handle increased load or growing datasets.

Talk about your hands-on experience with Kubernetes for container orchestration, emphasizing how you've deployed, scaled, and managed applications in a containerized environment. Mention specific challenges you've faced and how you've overcome them.

Be honest about the challenges, such as efficiently managing data loading or handling complex queries. Share how you addressed these challenges, showcasing problem-solving skills and technical expertise in optimizing GraphQL APIs.

Discuss the resources you use for continuous learning, such as following industry blogs, participating in webinars, or being involved in relevant communities and meetups. Emphasize your commitment to staying informed about emerging technologies and best practices in the field.