Generative AI Engineer (RAG)

Generative AI Engineer (RAG)
📍 Location: Gurugram (Onsite)
💼 Experience: 2–5 Years
🕒 Employment Type: Full-Time
About the Role
We are seeking a talented Generative AI Engineer with expertise in RAG (Retrieval-Augmented Generation), Speech-to-Text (STT), and Text-to-Speech (TTS) technologies. The ideal candidate will build intelligent AI assistants, conversational applications, and enterprise-grade GenAI solutions leveraging modern LLMs and cloud AI platforms.
Key Responsibilities
Design, develop, and deploy Generative AI applications using Large Language Models (LLMs).
Build and optimize RAG pipelines for enterprise knowledge retrieval and question-answering systems.
Develop and integrate Speech-to-Text (STT) and Text-to-Speech (TTS) solutions for voice-enabled AI applications.
Integrate LLMs such as OpenAI GPT, Claude, Gemini, and Llama into production environments.
Develop AI workflows using LangChain, LangGraph, and vector databases.
Create APIs and backend services to support AI-powered applications.
Monitor, evaluate, and improve AI model performance, latency, and response quality.
Collaborate with product, engineering, and business teams to deliver scalable AI solutions.
Required Skills
2–5 years of software development experience with strong Python programming skills.
Hands-on experience with RAG architectures, vector databases (Pinecone, Weaviate, Chroma, FAISS, etc.).
Experience with LLM frameworks such as LangChain, LangGraph, LlamaIndex, or similar.
Knowledge of OpenAI, Anthropic Claude, Gemini, or open-source LLMs.
Experience with STT/TTS technologies (Whisper, Deepgram, Azure Speech, Google Speech, ElevenLabs, etc.).
Understanding of embeddings, prompt engineering, fine-tuning, and AI evaluation techniques.
Familiarity with cloud platforms such as AWS, Azure, or GCP.
Experience with REST APIs, Docker, and deployment of AI applications.
Preferred Qualifications
Experience building AI chatbots, copilots, or voice AI solutions.
Knowledge of MLOps, model monitoring, and scalable AI deployment.
Exposure to agentic AI systems and multi-agent frameworks.

Work arrangement
Hybrid