Conversational AI Developer

Overview

On Site
Depends on Experience
Contract - W2
Contract - 24 Month(s)

Skills

NLP
LLMs
OpenAI
Microsoft Bot Framework
Watson Assistant
LangChain
DSPy
BotPress
NLU
NLG
Conversational AI
Chatbot

Job Details

This role involves designing, developing, and deploying intelligent virtual assistants and chatbots across customer-facing and enterprise workflows. You will work closely with product, data science, and engineering teams to build natural, context-aware, multi-turn conversation flows using cutting-edge LLMs, NLU/NLG models, and retrieval-augmented generation (RAG) architectures.

Key Responsibilities:

- Design, build, and deploy LLM-powered conversational experiences for web, mobile, and enterprise apps.

- Implement and optimize NLU/NLG models (using Hugging Face, spaCy, Rasa, etc.) for intent recognition, entity extraction, and dialogue act prediction.

- Integrate RAG pipelines (LangChain, LlamaIndex, or custom orchestration) to power context-aware conversations using enterprise knowledge bases.

- Build custom agents using frameworks like LangChain, DSPy, or Crew AI with tool integration, memory, and multi-step reasoning.

- Develop, fine-tune, or prompt-engineer LLMs (GPT, LLaMA, Claude, etc.) for domain-specific understanding and response generation.

- Design robust fallback, escalation, and intent disambiguation strategies to ensure resilient dialogues.

- Collaborate with front-end/backend teams to integrate bots into products using APIs, FastAPI, Flask, or GraphQL.

- Monitor and improve assistant performance using conversation analytics, A/B testing, and user feedback loops.

Required Skills & Experience:

- 3+ years of experience building conversational interfaces or NLP systems.

- Strong hands-on experience with Python and frameworks such as LangChain, DSPy, Rasa, BotPress, Dialogflow CX/ES, Microsoft Bot Framework, or Watson Assistant.

- Experience with LLMs (OpenAI, Cohere, Anthropic, Mistral, etc.), prompt engineering, and few-shot learning.

- Practical knowledge of NER, intent classification, and conversation state management.

- Familiarity with retrieval frameworks like FAISS, Pinecone, ChromaDB, and vector store querying.

- Cloud deployment experience on AWS / Google Cloud Platform / Azure (Lambda, Bedrock, EC2, S3, SageMaker, etc.).

- API design/integration experience, ideally with RESTful or GraphQL services.

- Strong understanding of conversation UX, fallback design, and personalization.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.