Senior Backend Engineer - Java

In 2024, Google selected LearnTube for its AI First Accelerator, further fueling our growth. Alongside investment from funds of visionaries like Reid Hoffman and Naval Ravikant, we set out on a mission to reimagine how billions will learn and grow.

Want to be the driving force behind our next wave of growth?

As a Senior Backend Engineer at LearnTube. ai you will ship the backbone that powers 4 million learners in 64 countries - owning APIs that crunch 1 billion learning events & the AI that supports it with <200 ms latency.

What You'll Do:

• Build and scale Java (Spring Boot) microservices handling ~800 requests/second today, growing to 3x capacity within a year while holding sub-200ms p95 latency
• Power the quiz-scoring and AI-tutor engines that process millions of learning events every day
• Design scalable, secure backend systems using Postgres, MongoDB, Redis, and SQS/Kafka
• Write high-performance, concurrent code and tune the JVM (GC, memory, thread pools) for low-latency workloads
• Deploy containerized services via NGINX on AWS and GCP Kubernetes infrastructure
• Own GitLab CI/CD pipelines that let us ship to production several times a week
• Drive reliability toward 99.9% uptime with Prometheus and Grafana monitoring
• Build LLM inference and vector-search endpoints in collaboration with the AI team
• Take features from design to production within two weeks

Must-Have Requirements:

• 3+ years of backend development in Java (11+, ideally 17/21) with Spring Boot in production
• Strong grasp of concurrency, multithreading, and JVM performance tuning
• Hands-on experience with Docker and container orchestration
• Production experience with GitLab CI/CD and AWS (EC2, S3, SQS) or GCP (GKE/Compute)
• SQL/NoSQL expertise (Postgres, MongoDB) and solid system design fundamentals
• Experience designing RESTful APIs and event-driven / message-queue based architectures

Nice-to-Have Skills:

• Kubernetes at scale, Terraform
• Kafka or other streaming platforms
• Reactive programming (Spring WebFlux, Project Reactor) or Kotlin
• AI/ML inference services experience (LLMs, vector databases)
• Observability tools (Prometheus, Grafana, OpenTelemetry)

How We Hire No take-home. No whiteboard trivia. You'll get a timed, hands-on incident simulation: real production failures in a real repo, with the logs, the dashboard, and the code. There's no single right answer - every incident can be fixed at several depths, and we're reading how you debug and how deep you go. Here's the link for you to get started on it - https://workat.learntube.ai/tech/java