Principal Automation Developer

About The Role

TTEC is scaling its Automation & AI portfolio to move beyond manual, ticket-by-ticket process work toward an agentic framework that can right-source work across business units. This role is central to that shift — building the multi-agent systems, integrations, and evaluation rigor needed to deploy AI agents safely and at scale across TTEC's technology and business operations.

What You'll Do

• Design and implement multi-agent workflows using Google Agent Development Kit (ADK), including agent orchestration, tool routing, and memory/context management
• Build integrations using Model Context Protocol (MCP) to expose internal systems (Jira, SharePoint, ServiceNow, internal APIs, etc.) as discoverable tools that agents can call at runtime
• Develop and maintain evaluation frameworks to measure agent accuracy, task completion rate, and safety — both pre- and post-deployment
• Design, build, and optimize conversational and task-based agents in Microsoft Copilot Studio, integrating with Power Platform (Power Automate, Dataverse) and enterprise data sources
• Apply strong prompt engineering practices — structured prompting, few-shot design, guardrails, and iterative evaluation — to improve agent reliability and reduce hallucination/drift
• Architect and deploy agentic solutions on Google Cloud Platform (GCP) (Vertex AI, Cloud Functions, Cloud Run, IAM) with attention to scalability, cost, and security
• Partner with business unit stakeholders to identify manual, repeatable processes suitable for agent-based automation, translating business requirements into technical agent designs
• Collaborate with the Automation & AI team on reusable patterns, shared tooling, and governance standards (security review readiness, access control, data handling)
• Contribute to internal enablement — documentation, training, and knowledge transfer so the broader team can build and extend agentic solutions
• Monitor deployed agents in production, iterating based on performance data, user feedback, and evaluation results
  
  
  

• Hands-on experience building with Google Agent Development Kit (ADK) or comparable agent orchestration frameworks (LangGraph, AutoGen, CrewAI)
• Practical experience with Model Context Protocol (MCP) — building or consuming MCP servers/tools
• Strong prompt engineering skills, including evaluation-driven prompt iteration
• Experience with Microsoft Copilot Studio for building and deploying enterprise agents/bots
• Working knowledge of GCP services relevant to AI workloads (Vertex AI, Cloud Functions/Run, IAM, logging/monitoring)
• Solid software engineering fundamentals — Python (preferred), API design, version control
• Understanding of LLM evaluation methodology: accuracy, task completion, safety/guardrail testing