AI Architect || Denver. CO (Onsite)

Role: AI Architect Agentic Workflows & Customer Journey

Location: Denver, CO (Onsite)

Duration: long term

Role Overview:

Customer is seeking an AI Architect to lead the technical design, orchestration, and implementation of our next-generation Agentic AI ecosystem. Reporting into the Customer Journey AI team, you will design the blueprints that allow autonomous, goal-oriented AI agents to execute complex, end-to-end workflows across all customer touchpoints-including digital portals, the Spectrum TV App, conversational IVR, messaging, and retail/field operations.

You will build the framework that transitions our customer experience from static chatbots to proactive, autonomous agents capable of real-time reasoning, tool use, and safe backend integration.

To implement Agentic AI workflows across all customer journeys, an AI Architect would need to bridge the gap between cutting-edge LLM orchestration and massive legacy telecom architectures (like BSS/OSS, CRM, and billing systems).

Unlike a traditional data science role, this architect focuses on multi-agent systems, real-time tool use, and safety guardrails to ensure autonomous agents can safely resolve customer issues (like billing disputes or network troubleshooting) without human intervention.

Core Responsibilities:

• Agentic Framework Architecture: Design and implement a scalable, highly available architecture for multi-agent systems, utilizing state-of-the-art frameworks (e.g., LangGraph, AutoGen, CrewAI, or proprietary orchestration layers) to handle complex customer intents.
• Context & State Management: Architect robust, low-latency state-management systems that allow AI agents to maintain context across multi-session, multi-channel customer journeys (e.g., transferring a digital chat context seamlessly to a live call center agent).
• API & Tool Integration (Function Calling): Design safe, secure, and standardized interfaces enabling LLM agents to accurately call tools, query databases, and execute actions within Charter's legacy BSS/OSS, billing platforms (e.g., CSG/Amdocs), and network telemetry systems.
• Guardrails & Deterministic Safety: Build and maintain deterministic validation layers, evaluation pipelines, and guardrail frameworks (e.g., NeMo Guardrails, Llama Guard) to ensure agent behavior complies with strict telecom compliance, privacy laws, and brand guidelines.
• RAG & Memory Architecture: Optimize Retrieval-Augmented Generation (RAG) pipelines and vector database infrastructure to give agents instantaneous access to thousands of internal knowledge bases, equipment manuals, and structured account schemas.
• Cross-Functional Technical Leadership: Collaborate closely with Software Development, Data Engineering, DevOps, and Product teams to transition legacy customer touchpoints into AI-native interfaces.

Required Qualifications & Technical Skills

• Experience: 10+ years in enterprise software architecture, with at least 3+ years specifically dedicated to production-grade AI/ML systems and LLM orchestration.
• Agentic Orchestration: Proven track record of deploying systems utilizing multi-agent collaboration, autonomous reasoning (ReAct patterns), and complex function calling.
• AI Stack: Advanced proficiency with Python, vector databases (e.g., Pinecone, Milvus, pgvector), and LLM frameworks. Experience with open-source and commercial models (OpenAI, Anthropic, Llama, Mistral).
• Data & Cloud Architecture: Deep experience with AWS or Google Cloud AI infrastructure, streaming data pipelines (Kafka, Flink), and caching strategies (Redis) for low-latency inference.
• Integration Expertise: Familiarity with integrating AI layers on top of high-transaction enterprise architectures, REST/GraphQL APIs, and microservices.

Preferred Qualifications:

• Telecom Domain Knowledge: Prior experience working with telecommunications customer data models, billing systems (Amdocs, CSG), or network operations.
• Advanced Evaluation Frameworks: Experience setting up automated evaluation rigs (e.g., Ragas, TruLens) to continuously score agent accuracy, hallucination rates, and task completion metrics in production