AI Solution Architect
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Job Location:
Chicago, IL - USA
Monthly Salary:
Not Disclosed
Posted on:
6 hours ago
Vacancies:
1 Vacancy
Job Summary
Role: AI Solution Architect
Skills: Core AI & GenAI Architecture & Design LLM driven workflows agentic frameworks API Cloud Platforms
Experience: 12 18 years (with 3 5 years in GenAI / LLM based systems)
Location: Chicago IL- day 1 on-site
Role Summary
The AI Solution architect will define end to end 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 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 GCP) and secure enterprise deployments.
- Knowledge of observability monitoring and cost management for AI systems.
