AI Architect - AIOps

Job Description:

The candidate will have responsibilities across the following functions:

AI Engineering, Agentic Capability, and Product Leadership:

- Lead the design, build, and operationalization of agentic AI capabilities for event correlation, incident triage, service health assessment, knowledge retrieval, decision support, and bounded remediation.

- Own the product strategy, roadmap, backlog, release approach, and value measures for the TOC agentic capability.

- Design human + AI workflows with clear reasoning, planning, tool use, memory, identity, traceability, and auditability.

- Apply strong engineering judgement on code-first, low-code, multi-agent, and integration patterns based on risk, scale, control, and operational value.

- Ensure every AI capability is measurable, supportable, production-ready, and safe to operate in live service environments.

- Drive event-driven operations by treating logs, alerts, events, traces, service state, and customer impact signals as first-class operational data.

- Improve telemetry quality, signal ownership, service mapping, dependency data, and operational context so AI and humans can reason from reliable evidence.

- Create decision-oriented views that show what is at risk, what is credible, what action is safe, and what customer or business impact is likely.

- Use operational data to identify reliability trends, leading indicators, toil reduction opportunities, and commercial or business impact where relevant.

TOC Strategy, Operating Model, and Service Design:

- Define the TOC agentic strategy and operating model, covering authority, workflows, incident handling, roles, governance, and execution boundaries.

- Establish the TOC as an event-driven control plane for live operational risk and customer-impacting technology events.

- Create a phased roadmap from MVP to scale, proving value early and building confidence for wider adoption.

- Clarify where TOC capability starts and stops, keeping product, platform, infrastructure, and support team accountability explicit.

- Use AI, automation, and operational context to improve incident detection, triage, escalation, recovery, and root cause learning.

- Reduce alert noise, duplicated effort, manual triage, and avoidable operational toil.

- Ensure the capability works across hybrid environments, including legacy platforms, cloud services, SaaS, networks, booking, and clinical systems where they are in scope.

- Build credibility by demonstrating measurable improvements in live service outcomes, not just prototypes.

- Create strong collaboration with product and domain owners so telemetry, automation, and delegated authority are used without weakening team accountability.

- Build the internal skills, standards, and support model, and run-operate-improve disciplines needed to sustain the capability.

- Link roadmap priorities to measurable improvements in service reliability, operational effectiveness, and business impact.

Requirements:

- Experience in AI engineering, agentic systems, automation, and observability, with a track record of applying these capabilities to improve live service outcomes.

- Strong understanding of observability, telemetry, event management, service mapping, operational data quality, and the use of signals to support decision-making.

- Experience introducing AI, automation, or AIOps capability into operational environments in a way that is safe, measurable, supportable, and adopted by users.

- Credibility in live-service environments, with enough technology operations knowledge to understand incidents, hand-offs, escalation, service continuity, and operational risk.

- Strong stakeholder leadership and communication skills, with the ability to influence senior leaders, engineers, operators, control functions, and suppliers.

- Experience building or leading multidisciplinary teams through transformation, ambiguity, and organizational change.

Particularly Relevant Experience:

- Experience with ServiceNow as the operational system of record, including incident and workflow integration.

- Experience with Google Cloud or comparable cloud platforms used for agentic and data workloads, including familiarity with patterns such as Gemini Enterprise Agent Platform, Python-based ADK development, Cloud Run services, managed or proxied MCP access, Apigee, BigQuery, Vertex AI, AlloyDB, GCS, and secure API mediation.

- Experience operating in regulated or high-control environments where resilience, auditability, customer impact, and control discipline are critical.

Skills

Artificial Intelligence, Machine Learning, Agentic AI, ServiceNow, Vertex AI, BigQuery, Python, Cloud, Generative AI, Observability Services