Senior AI Backend Engineer

Location

Worldwide

Posted

26 days ago

Salary

0

Seniority

Senior

Job Description

Senior AI Backend Engineer

BasharSoft

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

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