About The Position

Build scalable AI-powered backend systems that form the backbone of our client solutions. You'll architect robust APIs, integrate multiple AI providers with intelligent fallback strategies, and optimize real-time workflows that handle AI processing at scale.

Requirements

  • 5+ Years of Experience
  • Python & Frameworks: Django and/or FastAPI in production, async Python (asyncio, async/await)
  • AI Integration: LLM APIs (OpenAI, Gemini, etc.), prompt engineering, token/cost optimization
  • Databases: PostgreSQL (query optimization, indexing), Redis (caching, queuing)
  • Async Processing: Celery or equivalent, message brokers (RabbitMQ, Redis, SQS)
  • DevOps: Docker, Git, cloud platforms (AWS/GCP), CI/CD pipelines
  • API Design: RESTful principles, authentication, rate limiting, error handling

Nice To Haves

  • Server-Sent Events (SSE) or WebSocket implementation
  • Vector databases (Pinecone, Weaviate, pgvector) and RAG architectures
  • MongoDB or NoSQL experience
  • Multi-provider AI abstractions and failover strategies
  • GraphQL, workflow orchestration (Temporal, Airflow), Kubernetes
  • Observability tools (Datadog, Sentry, ELK)
  • LangChain, LlamaIndex, or similar frameworks

Responsibilities

  • Integrate and manage multiple AI providers (LLMs, image/video generation) with smart fallback and cost optimization
  • Design and optimize prompt engineering strategies for consistent, high-quality AI outputs
  • Build high-performance REST/GraphQL APIs using Django and FastAPI with async processing
  • Develop real-time features using SSE, WebSockets, and background task processing (Celery)
  • Architect scalable microservices with robust error handling, rate limiting, and monitoring
  • Optimize database queries, implement caching strategies, and design efficient data models
  • Implement RAG pipelines and vector embeddings for context-aware AI responses
  • Write comprehensive tests, maintain CI/CD pipelines, and conduct code reviews
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