IT Service and Platform Automation Architect
I bridge business strategy and modern service operations. I design service management, automation, agentic AI and observability solutions across the whole client lifecycle, from pursuit and bid support through transition, implementation and post-go-live optimization, and I partner with development and infrastructure teams who build them.
- Drawn by
- M. Lehman
- Title
- IT Service and Platform Automation Architect
- Location
- Ohio, USA (remote)
- Dwg no.
- ML-001
- Rev
- 2026.09
- Sheet
- 2 of 2
Solution architecture and pre-sales
- Design and present service management, automation and observability solutions to bid teams and clients during pursuits and RFP responses.
- Translate business, operational and non-functional requirements into tool-based architectures, with the value, trade-offs and outcomes spelled out.
- Contribute solution architectures, proposals, demonstrations and executive-level presentations.
Automation and integration
- Set automation strategy within service management: workflow, infrastructure and application automation, and event-driven remediation.
- Architect web services, APIs, integrations and automation frameworks on modern standards (REST, JSON, event streams), partnering closely with development teams on implementation.
- Keep automation and integrations aligned with business requirements as scope evolves.
- Provide L2 and L3 architectural guidance for integration and automation solutions, and mentor production support teams.
Agentic AI architecture and enablement
- Architect and pilot agentic AI in service management and operations, both to augment existing delivery roles and as a designed part of the platform.
- Context and retrieval management: RAG, graph-based retrieval, knowledge grounding and context engineering, so agents stay accurate to the client environment.
- Architectural direction for agent harnesses: tool and function-calling permissions, guardrails and prompt-injection defenses, in partnership with development teams.
- Ontologies and knowledge structures that agents and integrations reason over.
- Evaluation as part of the delivery lifecycle (accuracy, reliability, drift, regression), not a one-time check.
- Agent observability: tracing, monitoring and explainability of agent decisions, integrated with infrastructure and application observability.
- Integration mapping between agents, ESM and ITSM tooling, and enterprise systems.
- AI governance and responsible AI: data privacy, model risk, human oversight and auditability.
- Human-in-the-loop design: approval gates, escalation thresholds, and clear lines between automated and human-owned decisions.
- Cost, token usage and latency as architecture decisions: model selection, context sizing and caching.
- Helping clients and teams through the change and adoption questions agents raise for existing roles.
Observability and operational intelligence
- Observability and monitoring architectures spanning infrastructure, applications, services, AI components and user experience.
- Metrics, logs, traces, events and dependency mapping.
- Integrating monitoring with service management for proactive incident management, automated response and faster root cause analysis.
- AIOps and analytics to reduce noise, detect anomalies and support predictive and preventative operations.
Transition and delivery
- Take part in every phase of transition programs, from pursuit and design through implementation and stabilization.
- Planning, estimation, resourcing, risk and issue management, and technical decisions.
- Make sure delivered solutions meet architectural, operational and security standards and are supportable at scale.
Practice and capability development
- Build Service Integration and ESM practice capabilities.
- Create reusable assets: reference architectures, design patterns, accelerators, white papers and business cases, now extending into agentic AI patterns.
- Support innovation work in automation, agentic AI, observability and service operations.