Managed Services AI Platform Architect
| Hours | Full-time, Part-time |
|---|---|
| Location | Chicago, Illinois |
About this job
The Managed Services AI Platform Architect is a hands-on technical leader responsible for designing, building, and scaling AI-powered operational capabilities across the Managed Services organization. This role is primarily execution-focused, with direct accountability for delivering agentic AI workflows that improve service performance and efficiency at scale.
The architect will embed AI into core service delivery processes to drive measurable outcomes including incident deflection and ticket reduction, MTTR improvement, self-healing and automated remediation, knowledge-driven service delivery, and enhanced operational efficiency and client experience.
While contributing to architecture standards and roadmap alignment, success in this role is measured by platform delivery, adoption, and operational impact.
Key Responsibilities
AI Platform Delivery
- Design, build, and evolve the Managed Services AI platform - delivering production-grade AI capabilities integrated directly into service delivery workflows
- Lead the development of agentic AI solutions, including incident triage and classification, automated remediation and resolution, knowledge retrieval and summarization, and workflow orchestration and escalation
- Drive use cases from concept through prototype to production, ensuring real operational adoption
Agentic AI & Workflow Architecture
- Design and implement agent-based architectures including triage agents, resolution and remediation agents, RAG-based knowledge agents, and orchestration and multi-step workflow agents
- Define patterns for prompt design and structured outputs, tool integration and action execution, memory and state management, and human-in-the-loop controls
- Ensure AI workflows are observable, reliable, and continuously improving
Platform Integration & Operationalization
- Architect and integrate AI capabilities across core platforms including ITSM (ServiceNow), monitoring and observability tools, automation frameworks and runbooks, and knowledge management systems
- Embed AI directly into day-to-day operational workflows - not standalone solutions - designed for multi-tenant, scalable managed services environments
Standards, Guardrails & Roadmap
- Establish and maintain practical AI architecture standards and reusable patterns based on production usage
- Contribute to the Managed Services AI roadmap, grounded in delivered capabilities and business impact
- Define and enforce guardrails for safe automation: approval workflows and escalation paths, risk boundaries and controls, and observability and auditability
- Align with enterprise architecture standards where appropriate, while prioritizing speed and execution
Operational Outcomes & Metrics
- Drive measurable improvements across Managed Services operations: incident deflection rates, MTTR reduction, automation and self-healing coverage, ticket volume reduction, analyst and engineer productivity, and service quality and client experience
Execution Leadership
- Partner closely with Managed Services delivery teams, automation and platform engineering teams, and operations leadership
- Act as a player-coach - combining deep technical contribution with leadership and enablement
- Drive adoption and scaling of AI capabilities across the organization
Required Qualifications
- Proven hands-on experience designing and delivering AI/LLM-based systems (agentic AI, RAG, orchestration) and cloud-native platforms and integrations
- Strong background in IT operations, managed services, or service delivery environments, with experience in automation and workflow optimization
- Experience integrating with ITSM platforms (e.g., ServiceNow), observability and monitoring tools, and automation frameworks and scripting environments
- Ability to translate operational challenges into AI-driven solutions with a strong execution mindset focused on delivering measurable outcomes
Preferred Qualifications
- Experience building or deploying agentic AI systems in production environments
- Familiarity with AIOps, self-healing systems, and intelligent automation
- Experience working in multi-tenant or managed services delivery models
- Exposure to enterprise AI platforms, governance, and scaling patterns