Conversational AI Developer

Responsibilities

• Design, develop, and maintain conversational AI applications for chat and voice interactions.
• Create dialogue flows, conversation logic, and multi-turn interactions for virtual assistants and chatbots.
• Develop and integrate solutions using LLMs, NLP models, and Generative AI technologies.
• Create and optimize prompts, system instructions, and orchestration workflows to improve response quality and user experience.
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.
• Integrate conversational AI solutions with enterprise applications, APIs, databases, and third-party services.
• Build RESTful APIs and microservices to support chatbot functionality and backend integrations.
• Evaluate and improve chatbot performance using conversation analytics, user feedback, and AI evaluation metrics.
• Implement guardrails, content moderation, authentication, and security best practices for conversational AI applications.
• Collaborate with UX designers to improve conversation design, user engagement, and accessibility.
• Deploy, monitor, and maintain conversational AI applications using cloud and MLOps practices.
• Troubleshoot production issues and continuously optimize model performance and user satisfaction.

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.
• 3+ years of experience in conversational AI, chatbot development, NLP, or AI application development.
• Strong programming skills in Python; experience with JavaScript or TypeScript is a plus.
• Experience with NLP concepts such as intent recognition, entity extraction, text classification, and dialogue management.
• Experience working with Large Language Models (LLMs) and prompt engineering.
• Hands-on experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies.
• Experience building RESTful APIs using FastAPI, Flask, or similar frameworks.
• Familiarity with relational and NoSQL databases.
• Experience with Git, Docker, and cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

Preferred Experience

• Experience with conversational AI platforms such as Microsoft Copilot Studio, Azure AI Foundry, Google Dialogflow CX, Amazon Lex, IBM watsonx Assistant, or Kore.ai.
• Experience implementing Retrieval-Augmented Generation (RAG) using vector databases such as Pinecone, Milvus, Weaviate, Chroma, or Azure AI Search.
• Familiarity with model serving, API gateways, and AI deployment practices.
• Experience integrating speech recognition (ASR) and text-to-speech (TTS) technologies for voice assistants.
• Knowledge of AI agent frameworks, function calling, tool integration, and workflow orchestration.
• Experience implementing AI safety, governance, evaluation, and observability practices.
• Familiarity with CI/CD, Kubernetes, and MLOps concepts.