AI Solution Architect

Role : AI Solution Architect

Location: Chicago, IL 60604 (Onsite Job)

Skills: GenAI / Agentic AI architecture, Azure, Python, APIs, enterprise integrations, security/RBAC

Hiring Type: Video Interview & In-Person Interview

Role Summary:

The architect will define endtoend agent frameworks, ensure alignment with available enterprise environment, guardrails, and partner closely with engineering, QE, security, and compliance teams.

Job Description

Business & Stakeholder Leadership

• Translate business problems into agentdriven solution blueprints.
• Partner with senior stakeholders to identify highimpact use cases (automation, decision support, quality, operations).
• Provide executivelevel 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 (singleagent, multiagent, hierarchical, toolusing agents).
• Design LLMdriven workflows integrating reasoning, planning, memory, tools, and humanintheloop controls.
• Architect RAGbased and toolaugmented 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, nonfunctional, and ethical).

Core AI & GenAI

• Deep expertise in LLMs, prompt engineering, and reasoning frameworks.
• Handson 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 finetuning techniques (LoRA / QLoRA) and evaluation strategies.

Architecture & Engineering

• Strong background in distributed systems, APIs, microservices, and cloudnative 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.