Architect

Caylent is an AI-first cloud services company that helps organizations turn ambitious ideas into meaningful business impact. As an AWS Premier Tier Services Partner and a charter member of Anthropic’s Claude Partner Network, we combine deep expertise in AWS, artificial intelligence, and Anthropic’s Claude platform to help customers modernize their technology, build intelligent products, and move AI from experimentation into production.

Our capabilities span generative and agentic AI, cloud migration and modernization, cloud-native application development, data and analytics, DevOps, managed services, security and compliance, and customer experience transformation. At Caylent, our people always come first.

We are a fully remote global company with employees in Canada, the United States and Latin America. We celebrate the culture of each of our team members and foster a community of technological curiosity. Come talk to us to learn more about what it means to be a Caylien!

The Mission

We are seeking a talented Principal Architect to join our growing CXE (Contact Center Experience) practice. The right candidate is someone who combines deep hands-on Amazon Connect and conversational AI expertise with the technical leadership to own delivery of complex, AI-native contact center solutions end to end. Your mission will be to lead the architecture and technical delivery of Amazon Connect and AI agent solutions for our customers, providing expert guidance, managing technical risk, and ensuring that scope, deliverables, and objectives align to business outcomes and Caylent best practices while leading the Caylent technical team to deliver with excellence.

Your Assignment

AI Agent & Conversational AI Development

• Build AI-native self-service using Amazon Connect AI agents, Amazon Q in Connect, and Amazon Bedrock (Claude model family), implementing multi-agent orchestration patterns, with orchestrator agents delegating to task-specific agents and escalation paths to human queues
• Engineer prompts and tool-calling integrations at production scale: SearchProfiles lookups, case creation, external API tools, authentication flows, and guardrails against hallucination and verification loops
• Implement barge-in handling, fallback, and error-recovery behavior for voice AI, and validate it with turn-by-turn trace analysis
• Optimize inference cost and latency hands-on: model tier selection (Haiku vs. Sonnet vs. Opus), prompt caching, and token budgeting against containment-rate targets
• Diagnose AI agent performance using observability tooling (agent spans and CloudWatch) that correlates contact flow logs, conversation transcripts, AI agent spans, tool executions, and token usage to isolate latency, cost, and quality issues