Role: AI Solution Architect
Location: Chicago- Day 1 onsite
Experience: 12 18+ years
Type: Contract
Duration: Long Term Contract
Skills: GenAI / Agentic AI architecture, Azure, Python, APIs, enterprise integrations, security/RBAC.
Role Summary
The architect will define end to end agent frameworks, ensure alignment with available enterprise environments, guardrails, and partner closely with engineering, QE, security, and compliance teams.
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.
