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AI Solution Architect

Reveille TechnologiesChicago, IL🇺🇸United StatesPosted Sep 16, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Chicago, IL, United States
Posted
Yesterday
MicroservicesAWSAzureGoogle CloudLLMPython

Job Description

Business & Stakeholder Leadership

  • Translate business problems into agent‑driven solution blueprints.
  • Partner with senior stakeholders to identify high‑impact use cases (automation, decision support, quality, operations).
  • Provide executive‑level guidance on agentic AI adoption, maturity models, and roadmaps.
  • Support client conversations, RFPs, solution pitches, and thought leadership.

Architecture & Design

  • Define reference architectures for agentic AI systems (single‑agent, multi‑agent, hierarchical, tool‑using agents).
  • Design LLM‑driven workflows integrating reasoning, planning, memory, tools, and human‑in‑the‑loop controls.
  • Architect RAG‑based and tool‑augmented agents using enterprise data sources, APIs, and workflows.
  • Ensure scalability, resilience, observability, and cost optimization of agent platforms.

Governance, Risk & Guardrails

  • Establish AI guardrails covering safety, bias, explainability, auditability, and regulatory compliance.
  • Define agent lifecycle management (design, testing, deployment, monitoring, retirement).
  • Partner with Risk, Legal, Security, and QE teams to ensure model risk management (MRM) and enterprise readiness.
  • Drive standards for agent testing, validation, and certification (functional, non‑functional, and ethical).

Core AI & GenAI

  • Deep expertise in LLMs, prompt engineering, and reasoning frameworks.
  • Hands‑on experience with agentic frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, custom agent orchestration).
  • Strong understanding of RAG, embeddings, vector databases, and knowledge grounding.
  • Experience with fine‑tuning techniques (LoRA / QLoRA) and evaluation strategies.

Architecture & Engineering

  • Strong background in distributed systems, APIs, microservices, and cloud‑native architectures.
  • Proficiency in Python and familiarity with enterprise integration patterns.
  • Experience with cloud platforms (Azure, AWS, Google Cloud Platform) and secure enterprise deployments.
  • Knowledge of observability, monitoring, and cost management for AI systems.

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