We are looking for a Backend Engineer to drive the architecture and development of our AI-powered language learning platform. In this role, you will design and implement backend systems that directly impact millions of learners worldwide. You will architect scalable services that power real-time AI conversations, ensuring low-latency performance and reliability while building the foundation for our next generation of language learning features.
What Youll Do:
Design, develop, and maintain high-performance backend services and APIs (REST and gRPC) that power AI-driven conversational experiences.
Build and optimize asynchronous Python applications capable of handling real-time audio/text processing at scale.
Ensure seamless, low-latency integration between mobile and web clients and our AI backend platform.
Drive technical decisions on system architecture, focusing on low latency and fault-tolerance.
Collaborate with AI/ML, mobile, DevOps, and product teams to deliver end-to-end solutions that delight our users.
Work within CI/CD workflows to ensure smooth deployments and maintain high code quality standards.
Optimize system performance and resource utilization while maintaining reliability SLAs for our growing user base.
Requirements: Minimum of 7 years of experience designing and developing scalable, distributed backend systems in one or more modern programming languages (Python is a plus).
Deep expertise in Python concurrency and execution models (WSGI, ASGI, asyncio, multiprocessing, threading, GIL).
Proven track record building production-ready asynchronous Python applications serving high-volume traffic.
Strong experience with Pydantic and FastAPI in production environments.
Expertise in designing and implementing RESTful APIs and gRPC services with strong emphasis on versioning strategies and backward/forward compatibility.
Demonstrated ability to solve complex performance, scalability, and workload distribution challenges.
Proficiency with relational and non-relational database solutions (PostgreSQL, Redis, DynamoDB, MongoDB), including query optimization and data modeling.
Experience with event-driven architectures and message queuing systems (Kafka, RabbitMQ, AWS SQS).
Hands-on experience with Docker, understanding of CI/CD pipelines and methodologies, and working in Kubernetes/AWS-based deployments.
Strong proficiency with AWS cloud services and cloud-native architectures.
Understanding of observability practices (distributed tracing, metrics, logging) and experience with monitoring tools.
This position is open to all candidates.