Senior AI Backend Engineer
Summary
Design and build scalable AI-powered backend systems using Python, Django/FastAPI, and LLM APIs, with real-time features and vector embeddings for context-aware responses.
Role Description
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.
What You'll Do
- 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
Qualifications
- 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
Tech Stack
- Python 3.10+
- Django, FastAPI
- PostgreSQL, Redis, MongoDB
- OpenAI, Gemini
- Celery
- Docker
- AWS/GCP
Nice-to-Have
- 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