Conversational AI Developer

Summary

Bell is seeking a Conversational AI Developer to join an innovative, customer-focused team. You’ll build and improve virtual assistants that help Bell customers get answers and get things done, using Google’s CX Agent Studio and Dialogflow CX. This is a hands-on role for a curious, resourceful problem solver who wants to grow their skills in conversational AI, LLM-powered experiences, and AI agents. Working within an Agile framework, you’ll collaborate with senior developers, architects, conversational designers, and product owners to deliver solutions that evolve with our customers’ needs. 

Key Responsibilities

• Build, configure, and maintain conversational AI assistants using CX Agent Studio, and learn and contribute to Dialogflow CX flows, intents, pages, and webhooks.
• Implement conversation flows and dialogue logic based on designs from conversational designers and architects.
• Develop integrations with backend services and APIs using JSON and other data interchange formats.
• Write, test, and refine prompts and agent instructions, and help manage the context (conversation history, retrieved knowledge, and tool outputs) that agents use to respond accurately.
• Implement and troubleshoot tool calling, connecting agents to APIs, data sources, and backend systems so they can complete tasks for customers.
• Support the implementation of LLM-based and agentic features, including knowledge retrieval and multi-step agent behaviours, under the guidance of senior team members.
• Explore and prototype with emerging agent frameworks and protocols (e.g., ADK, MCP, A2A) as the team evaluates them.
• Test and troubleshoot assistants, including end-to-end conversational flow testing, to identify and resolve defects and improve accuracy.
• Review performance data and customer interactions to find opportunities for continuous improvement, and help tune intents, prompts, and fallback handling.
• Contribute to multi-channel deployments (web, mobile, messaging, IVR) while following enterprise security, privacy, and governance standards.
• Document your work, participate in Agile ceremonies (stand-ups, sprint planning, retros, PI Planning), and track tasks in JIRA.
• Stay current on evolving conversational AI and LLM trends and share what you learn with the team. 

Critical Qualifications

• Strong problem-solving skills and a logical, analytical approach to debugging and improving systems.
• Foundational understanding of Natural Language Understanding (NLU) and Large Language Models.
• Working knowledge of prompt engineering: writing clear instructions, using examples, and iterating on prompts based on results.
• Basic understanding of context engineering: how conversation history, retrieved information, and tool results shape LLM behaviour and quality.
• Basic understanding of tool/function calling and how LLM agents interact with external APIs and systems.
• Experience with, or strong interest in learning, conversational AI platforms such as CX Agent Studio and Dialogflow CX (through work, academic projects, internships, or personal projects).
• Working knowledge of JSON and REST APIs.
• Programming experience in at least one language (e.g., Python, JavaScript/Node.js).
• Basic familiarity with cloud platforms (GCP preferred, Azure or AWS also relevant).
• Good written and verbal communication skills, with the ability to explain technical work clearly to teammates and stakeholders.
• Willingness to learn quickly, accept feedback, and work in a collaborative Agile team.
• Passion for AI-powered language models, chatbots, voice bots, and AI agents. 

Preferred Qualifications

• Bachelor’s degree (or college diploma) in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related field, or equivalent practical experience.
• 0-2 years of experience in software development, chatbot development, or a related area (co-op, internship, and project experience welcome).
• Basic understanding of or exposure to agentic AI concepts and frameworks, such as Google’s Agent Development Kit (ADK).
• Awareness of emerging agent standards, including Model Context Protocol (MCP) for connecting agents to tools and data, and Agent2Agent (A2A) for agent-to-agent communication.
• Exposure to RAG and knowledge retrieval approaches.
• Familiarity with testing and quality assurance practices, including evaluating LLM and agent outputs.
• Familiarity with JIRA and Agile/SAFe ways of working.
• Interest in or exposure to voice AI and speech recognition.
• Awareness of web accessibility principles.
• Bilingual (French/English) would be an asset.
• Experience in a telecommunications environment would be a bonus.