AI Conversation Engineer (Remote 100%)
Job Title: AI Conversation Engineer Remote 100% - Must be in EST or CST W2 Core Responsibilities 1. Conversation Design
- Map out user journeys and conversational flows
- Write intents, training phrases, prompts, transitions, and error handling
- Design tone, personality, and overall user experience (UX)
2. NLP / NLU Engineering
- Train models to classify user intents
- Define entities and parameters
- Improve accuracy using analytics and conversation logs
3. Bot Development
- Build flows, forms, conditions, and API calls within tools like Dialogflow CX
- Integrate backend systems (CRMs, ticketing, databases)
4. Integration & Automation
- Connect the bot to external services using APIs
- Use cloud functions, workflows, and serverless infrastructure
- Connect to channels (web chat, phone, SMS, contact center)
5. Testing & Optimization
- A/B test reply strategies
- Analyze transcripts
- Measure metrics like containment rate, intent accuracy, and task completion
Skills 1. Conversational Design Skills These skills relate to designing natural, helpful, and efficient dialog flows. What you need:
- User journey mapping understanding how users think and what they need
- Conversation flow design building multi-step, multi-intent dialog paths
- Prompt and response writing crafting clear, concise, and friendly bot messages
- Error handling & fallback strategies guiding users when the bot gets confused
- Bot personality & tone ensuring consistent voice across interactions
These skills blend UX writing + UX design + product thinking. 2. NLU / NLP Understanding Conversation Engineers don't need to be data scientists, but they do need to understand how language models work. What you need:
- How intents, entities, training phrases, and parameters work
- How NLU confidence scoring works
- How to improve intent detection accuracy
- Understanding of LLM prompting and guardrails
- Basic understanding of machine learning concepts (classification, embeddings, etc.)
Experience with platforms like Dialogflow CX, LLMs, or Watson Assistant helps here. 3. Platform Expertise (Dialogflow CX, CCAI, etc.) You need hands?on ability to build, manage, and optimize actual systems. Skills:
- Building flows in Dialogflow CX
- Using webhooks and fulfillment
- Configuring parameters, forms, conditions, and transitions
- Channel integrations (web, voice, SMS, contact center)
- Working with tools like Contact Center AI (CCAI) for voicebots
- Understanding versioning, environments, and testing tools in CX
This is the "craftsmanship" part of being a conversation engineer. 4. Software Engineering Basics You don't have to be a full-stack engineer-but you must be comfortable with backend logic. Key technical abilities:
- JSON (100% required for APIs and webhook payloads)
- REST APIs reading docs, sending/receiving data
- Basic scripting (JavaScript, Python, or Node.js commonly used for fulfillment)
- Understanding of serverless functions like Cloud Functions
- Error-debugging skills (HTTP status codes, logs, timeouts)
You should be able to read and write small pieces of code confidently. 5. Cloud & Integration Skills Since most conversational AI lives in the cloud (like Google Cloud Platform), integration knowledge is essential. For Google Cloud Platform specifically:
- Cloud Functions execute business logic
- Firestore / BigQuery / Cloud SQL store and retrieve data
- IAM & service accounts controlling permissions
- Logging & Monitoring analyzing issues and optimizing performance
Knowing how APIs and cloud services work is what connects the bot to the company's real systems. 6. Analytics & Optimization Great conversation engineers care deeply about improving performance, not just building. Key analytical skills:
- Reading conversation transcripts
- Using analytics tools (Dialogflow, BigQuery dashboards, etc.)
- Identifying intent confusion or user friction points
- Running A/B tests
- Improving containment rate, task completion, and NLU accuracy
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