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In office (Gurgaon)

First seen by Alion on Oct 9, 2026.

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EngiNeo is a technology implementation company expert in travel and expense management, ERP consulting, and more, partnering with leading brands like JCB, Duroflex, Muthoot Microfin, etc. to optimize operations.

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.
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