About the role
We are looking for a skilled AI Engineer with hands-on experience in LLMs, conversational AI, and voice AI systems. The candidate will be responsible for building and deploying scalable AI applications, RAG pipelines, and real-time conversational systems using modern AI frameworks and backend technologies.
Responsibilities:
- Design, develop, and deploy LLM-based AI applications and conversational AI systems.
- Build and optimize RAG pipelines, embeddings, and vector database integrations.
- Develop backend AI services using Python frameworks like FastAPI or Flask.
- Work on Voice AI applications, including STT/TTS and real-time conversational systems.
- Implement real-time communication using WebSockets or streaming APIs.
- Optimize AI application latency, context handling, and response quality.
- Integrate AI models with scalable backend architectures and cloud platforms.
- Monitor, evaluate, and improve AI system performance and output accuracy.
- Collaborate with product and engineering teams for deployment and production support.
- Work on prompt engineering, workflow optimization, and AI system enhancements.
Requirements:
- Strong experience with Python and AI backend development.
- Hands-on experience with LLMs, RAG pipelines, embeddings, and vector databases.
- Experience with LangChain, OpenAI APIs, or similar AI frameworks.
- Knowledge of FastAPI/Flask and REST APIs.
- Experience with Voice AI systems (STT/TTS) and conversational AI applications.
- Understanding of WebSockets, streaming APIs, and real-time systems.
- Familiarity with cloud platforms like AWS/GCP/Azure.
- Good understanding of prompt engineering and AI optimization techniques.
Preferred Skills:
- Experience with Whisper, LangGraph, Pinecone, FAISS, ChromaDB, or similar tools.
- Knowledge of Docker, CI/CD pipelines, and scalable microservices architecture.
- Experience with monitoring and evaluation frameworks for AI outputs.