Senior AI Backend Engineer - Remotely

Job Description

The Role
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

Job Requirements

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.
   

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