We are looking for an experienced Conversational AI Engineer with strong expertise in Python, NLP, NLU, LLMs, and chatbot development to design and build intelligent conversational applications and enterprise-grade virtual assistants.
The ideal candidate should have hands‑on experience developing context‑aware conversational solutions, integrating LLMs, managing multi‑turn conversations, and delivering scalable AI‑powered chatbot and voice assistant applications.
- Design, develop, and deploy intelligent chatbots, virtual assistants, and conversational AI applications.
- Build NLP/NLU solutions for intent recognition, entity extraction, sentiment analysis, and dialogue management.
- Develop LLM-powered conversational applications using OpenAI GPT, Google Gemini, Anthropic Claude, Llama, or similar models.
- Implement multi‑turn conversations, contextual memory, session management, and dialogue flows.
- Build and integrate RAG‑based conversational applications for context‑aware and knowledge‑driven responses.
- Develop AI‑powered workflows using LangChain, LangGraph, and other conversational AI frameworks.
- Integrate chatbots and virtual assistants with REST APIs, CRM, ERP, and enterprise applications.
- Implement function calling, tool calling, and AI agent capabilities.
- Optimize chatbot performance, response accuracy, latency, scalability, and reliability.
- Implement conversation analytics, monitoring, evaluation, and continuous improvement.
- Ensure responsible AI practices, data privacy, security, and hallucination mitigation.
- 7+ years of experience in software development / AI engineering.
- Strong hands‑on experience in Python programming.
- Proven experience in Conversational AI and Chatbot Development.
- Strong understanding of NLP, NLU, Intent Recognition, Entity Extraction, and Dialogue Management.
- Hands‑over, what? – actually it's "Hands‑on experience with LLMs such as GPT, Gemini, Claude, or Llama.".
- Experience with frameworks such as Rasa, LangChain, LangGraph, or similar.
- Strong knowledge of Prompt Engineering and Context Management.
- Experience implementing RAG, Embeddings, Vector Databases, and Semantic Search.
- Experience building multi‑turn conversational systems and contextual memory.
- Strong experience integrating AI applications with REST APIs and backend systems.
- Experience deploying and maintaining production‑grade conversational AI solutions.