Quran Senior Backend Engineer opportunity — Spring Boot
- Level: Senior (3–5 years building and operating production backends)
- Employment: Full-time · Remote / Hybrid
Tech Stack
- Core Stack: Java 21 or Kotlin, Spring Boot 3.3+, Spring Security (OAuth2 / JWT), Spring Data JPA / Hibernate, PostgreSQL 16 (JSONB, GIN), Flyway, SSE / NDJSON streaming, OpenAPI / Swagger, Maven or Gradle, Git, Docker, GitHub Actions, Render, JUnit 5.
- On the Roadmap: gRPC / Protobuf, Redis, pgvector & Qdrant, Resilience4j, Kafka or RabbitMQ, LiveKit, OpenTelemetry, Testcontainers.
What We Are Building
A platform for learning to recite the Qur'an correctly:
- The Recitation Engine: A student recites; the engine returns per-letter pronunciation errors, tajweed rule findings, and Madd and Ghunnah durations measured against the reciter's own tempo, complete with letter-level timings for playback.
Core Responsibilities
- Build the Spring Boot Backend: Lead API design, PostgreSQL schemas, versioned migrations (Flyway), and robust Spring Security (role-based access control for students, teachers, examiners, and scholars, with refresh-token rotation). Define clean microservice boundaries between Spring Boot and Python AI services.
- Model the Curriculum: Architect data models for courses, lessons, per-point mastery, progress, and certification readiness criteria.
- Own the Streaming Path: Build and maintain chunked NDJSON / SSE endpoints delivering two-stage recitation feedback and streamed agent answers to the Flutter client, with graceful handling of partial failures and client disconnects.
- Audio Pipeline Engineering: Handle mobile audio uploads, format/sample-rate normalization, storage, retention, and HTTP Range streaming so learners can seek smoothly without downloading entire chapters.
- Asynchronous Processing: Architect an asynchronous job tier for audio dubbing and long-running analysis, keeping progress visible to clients while strictly enforcing recitation-safety invariants.
- Performance & Cost Optimization: Implement strategic caching (Mushaf pages, āyah text, tafsir), admission control, rate limiting, and idempotency around metered model calls, including cache pre-warming ahead of peak traffic.
- Observability & Debuggability: Set up structured logging with request correlation IDs from day one, tracking spend-critical paths, and implementing distributed tracing across Flutter → Spring Boot → Python AI Services → PostgreSQL.
- Testing & API Evolution: Write robust integration tests against real dependencies (Testcontainers), deterministic tests around the AI boundaries, and load tests. Ensure backward-compatible API versioning so mobile updates never break.
- Collaborative Leadership: Review pull requests, document major technical decisions (ADRs), and collaborate directly with Python AI engineers on service contracts and Quranic scholars on rule specifications.
Qualifications & Requirements
- Resilient Distributed Architecture: Experience handling unreliable or slow downstream services (retries with exponential backoff, circuit breaking via Resilience4j, bulkhead isolation, and idempotency keys).
- Caching & High Throughput: Hands-on experience with Redis (distributed caching, rate limiting, token buckets) and in-process caching patterns.
- Data & Binary Streaming: Deep understanding of handling large binary payloads, object storage, and streaming without exhausting server memory.
- Automated Testing: Strong discipline with JUnit 5, Mockito, and testing against real databases/containers rather than purely mocked persistence layers.
- DevOps & Infrastructure: Solid foundation in Docker, Linux, CI/CD pipelines, and cloud deployment environments.
- Cost Consciousness: Pragmatic mindset toward cloud infrastructure and external AI API usage, instinctively optimizing cost-per-request.
- Communication: Excellent written English for documenting architectural choices, writing detailed PR descriptions, and coordinating across remote teams.
Preferred / Strong Pluses
- Audio Engineering: Hands-on experience with codecs (Opus, AAC, PCM), FFmpeg, and audio normalization.
- Modern IPC: Practical experience with gRPC and Protocol Buffers for polyglot service communication (Spring Boot ↔ Python).
- AI/LLM Integration: Experience integrating LLM/ASR APIs, managing token budgets, streaming inference results, and deploying production RAG workflows (pgvector, Qdrant, chunking, grounding evaluations).
- Real-time Media: Familiarity with LiveKit / WebRTC for live virtual classrooms or oral exam sessions.
- Enterprise Observability: Experience instrumenting services using OpenTelemetry, Prometheus, and Grafana.
