5-10 Years Experience
Core Responsibilities
- Designs NLU/NLP models, intent frameworks, and entity taxonomies; architects virtual agent solutions
- Configures and integrates conversational AI platforms: Genesys, NICE, Google CCAI, Amazon Connect
- Integrates virtual agents with CRM, knowledge base, and backend systems
- Google CCAI, AWS, or platform-specific certification preferred
Contact Center Experience
- Hands-on experience deploying conversational AI in CC: IVR containment, virtual agent deflection, agent assist
- Familiar with telephony integration for CC virtual agents: SIP/PSTN, DTMF fallback, call transfer with context
- Experience integrating NLU/NLP with CCaaS routing engines for intent-based routing
Role:
Using expertise in Large Language Models (LLMs), Natural Language Understanding (NLU), and conversational design, resource will craft effective AI workflows, ensuring quality, performance, and seamless operation. They will design, prototype, and validate conversational solutions for AI agent deployments and guide customers and partners in maximizing the value of AI solutions.
Responsibilities:
● Scope and implement AI Agent deployments, providing strategic advice and execution support to customers and partners.
● Leverage your knowledge of LLM internals (e.g., embeddings) to analyze customer requirements and design precise prompts for reliable, user-aligned behavior.
● Simplify complex workflows and processes into digestible conversational components, enabling LLMs to handle challenging tasks effectively
● Fine-tune conversational flows and voice output (e.g. SSML, lexicons, regex) to align with customer brand standards
● Work closely with Agent Integration Engineers and Forward Deployed Engineers to connect customers’ systems with Agent Platform via APIs
● Identify and solve blockers together with other departments at (e.g. Product, Agent Integration Engineering, or Sales) and the customer
● Apply structured testing approaches to validate AI agent behavior, quality, and performance under real-world conditions
● Document best practices, how-to guides, and product capabilities for internal and external audiences, representing the expertise of the Agent Architect team
Requirements:
● 3+ years of experience in enterprise customer-facing roles, with proven expertise in conversation design and AI agent development
● Ability to analyse customer requirements and craft LLM prompts that align with desired conversational and functional outcomes
● Proficiency with advanced prompting strategies such as chain-of-thought prompting, few-shot learning
● Strong project and stakeholder management skills, with a passion for meeting milestones and communicating clearly
● Analytical and critical thinking skills, including experience with risk assessment and problem solving
● Solid understanding of data structures, system integrations, and enterprise APIs
● Exceptional attention to detail and logical thinking, with the ability to identify and address subtle nuances in conversational flows
● Experience planning and executing rigorous testing procedures to ensure AI agent performance at scale
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