Senior AI Backend Engineer - Remotely

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

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

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