AI Architect

Architect AI

About The Role

TTEC's App Delivery & Automation organization is in the midst of a meaningful transformation — moving from a traditional engineering model organized around technology stacks to an AI-first delivery engine organized around outcomes. Three distinct pillars now power this organization: Automation (RPA, shop floor), AI & Intelligence (bots, agentic AI, GenAI), and Product Engineering (SaaS products, desktop and business applications).

The AI Architect is a new role created to give this transformation a coherent technical spine. You will own the architecture layer that sits across all three pillars — defining how AI systems are designed, how platforms are selected and integrated, how data flows between systems, and how decisions made in one pillar stay consistent with decisions made in another.

This is not a strategy role that produces documents. It is a hands-on technical leadership role. You will design systems, review implementations, prototype new patterns, and be the person others come to when a hard architectural question needs a definitive answer. You report directly to the VP of App Delivery & Automation and carry director-level authority to set and enforce architectural standards across the organization. You must be located in Denver, Colorado

What You'll Own

AI platform architecture

Define and maintain TTEC's multi-model AI platform architecture across Anthropic Claude, Google Gemini / Vertex AI, and Microsoft Copilot Studio — including model selection criteria, API integration patterns, and platform governance boundaries
Establish standards for how agents are built and composed across Google ADK and MCP — including tool design, memory and context management, orchestration patterns, and agent-to-agent communication
Own the architectural decisions that govern how AI outputs are evaluated, monitored, and improved in production — including eval frameworks, observability instrumentation, and drift detection

Cross-pillar technical coherence

Ensure that the Automation, AI & Intelligence, and Product Engineering pillars share consistent integration patterns, data contracts, and security postures — preventing architectural drift as each pillar scales independently
Design the interfaces between deterministic automation (RPA, Power Automate) and probabilistic AI systems (agents, bots) — defining where handoffs happen, how errors propagate, and how humans stay in the loop
Maintain a living architecture reference — a practical, up-to-date view of how TTEC's AI systems connect, depend on each other, and evolve over time

AI governance & standards

Define and own TTEC's technical AI governance standards: prompt architecture guidelines, responsible AI design patterns, data handling requirements, and review criteria used by the ARB/SRB/RAIC intake process
Establish a tiered approach to AI system risk — distinguishing low-risk internal productivity tools from high-risk client-facing agentic systems — and ensure appropriate review rigor is applied to each tier
Lead architectural reviews for new AI initiatives, serving as the technical decision-maker within the governance intake process

Emerging technology leadership

Continuously evaluate new AI frameworks, model releases, and infrastructure patterns — translating findings into concrete, time-bound recommendations for the team's roadmap
Prototype architectural patterns before recommending them — you should be able to build a working proof of concept, not just describe one
Contribute to TTEC's AI Agent Hub architecture: the centralized platform through which employees discover and launch AI agents across Microsoft, Google, and Anthropic tooling

What You'll Bring To The Role

12+ years of software and systems architecture experience, with at least 4 years focused on AI/ML systems, LLM application architecture, or intelligent automation at enterprise scale
Deep, hands-on experience with large language model APIs and agent frameworks — you have built production agentic systems, not just evaluated them
Demonstrated ability to architect across multiple AI platforms simultaneously (e.g., Anthropic + Google + Microsoft) and make principled decisions about where each platform fits
Strong working knowledge of Model Context Protocol (MCP) — including designing MCP servers, exposing enterprise systems as tools, and integrating MCP into agent orchestration layers
Experience designing AI governance frameworks: risk tiering, review processes, responsible AI standards, and human-in-the-loop patterns for enterprise deployments
Ability to operate at multiple levels of abstraction — from whiteboard architecture to working prototype — without losing sight of either
Strong communication skills with both technical and non-technical audiences; comfortable presenting architectural recommendations to senior leadership

Preferred Experience

Hands-on experience with Google Agent Development Kit (ADK), LangGraph, or comparable orchestration frameworks for multi-agent systems
Experience architecting AI systems in BPO, contact center, or enterprise SaaS environments — understanding the operational constraints of high-volume, low-latency, compliance-sensitive deployments
Familiarity with RPA platforms (Power Automate, UiPath) and experience defining the architectural boundary between RPA and agentic AI
Experience with Azure AI services, Google Vertex AI, and the integration patterns that connect these platforms to enterprise data and identity systems
Exposure to shop floor or operational technology (OT) environments — understanding the unique constraints of AI systems that interact with physical operations
Prior experience in a formal architecture governance role (enterprise architect, principal architect, or equivalent)

How Success Is Measured

In the first 90 days, success looks like: a clear, documented view of TTEC's current AI architecture across all three pillars; identified gaps and inconsistencies; and a prioritized set of arc