AI Solution Architect

THE COMPANY

STACK INFRASTRUCTURE (STACK) provides digital infrastructure to scale the world’s most innovative companies. We are an award‑winning industry leader in building, owning, and operating highly efficient, cost‑effective wholesale, colocation, and cloud data centers. Each of our national facilities meets or exceeds the highest industry standards in all operational categories of availability, security, connectivity, and physical resilience.

STACK offers the scale and geographic reach that rapidly growing hyperscale and enterprise companies need. The world runs on data. Data runs on STACK.

STACK is seeking an AI Solution Architect to serve as the senior technical authority and delivery lead for enterprise AI solutions. This role owns the end‑to‑end design, engineering, and deployment of AI systems — spanning generative AI, LLM engineering, RAG pipelines, smart routing, agentic workflows, enterprise system integrations, intelligent layer development on Databricks, and production AI infrastructure — while driving technical adoption across the organization.

The AI Solution Architect is the builder and owns the data architecture, the Python code, the integrations, the Git Lab repositories, the deployment pipelines, and the entire intelligent layer with the technical reliability of every AI solution in production. This role owns the AI system architecture and engineering layer that sits after the data foundation layer all the way to business outcome.

This role reports directly to the Head of AI & Data Strategy and serves as the primary technical engineering authority for all AI solution delivery across the enterprise.

• Design, develop, and deploy production GenAI solutions including custom assistants, multi‑agent systems, and LLM‑powered workflow automation — with hands‑on ownership of every layer from prompt design through inference endpoint.
• Architect and implement smart LLM routing logic — designing multi‑model routing systems that dynamically select the right model based on query complexity, cost thresholds, latency requirements, and data residency constraints. Implement fallback chains, load balancing, and model arbitration patterns for production reliability.
• Build and optimise Retrieval‑Augmented Generation (RAG) pipelines end to end — including document ingestion strategy, chunking and overlap logic, embedding model selection and tuning, vector store architecture, hybrid retrieval design, reranking layers, and context window management for enterprise knowledge applications.
• Engineer and maintain prompt libraries, system prompts, chain‑of‑thought patterns, and prompt chaining logic for all production LLM applications — maintaining a versioned prompt registry in Git Lab with structured testing and evaluation before promotion.
• Build LLM fine‑tuning and alignment pipelines on Databricks MLflow — including supervised fine‑tuning (SFT), parameter‑efficient fine‑tuning (LoRA, QLoRA), and RLHF.
• Implement guardrails, content filtering, hallucination detection, and output validation layers to ensure AI responses meet safety, accuracy, and compliance standards established by the governance framework.

• Implement and deploy predictive AI solutions that surface forecasts and recommendations to business users — building on feature specifications and model evaluation criteria provided by the AI Data Scientist.
• Engineer the prescriptive AI layer — translating model outputs and recommendations into automated decisions, workflow triggers, and user‑facing actions within business systems.
• Build model serving infrastructure: inference endpoints, prediction APIs, caching layers, and fallback logic that ensure production models meet latency, reliability, and cost requirements.
• Integrate AI model outputs into operational workflows, automated alerts, and decision support tools so insights reach end users in context and at the right moment.

• Design and build the full agentic AI architecture for Phase III of the enterprise AI roadmap — including agent…