AI Solution Architect

The Technology Operations AI Lead is accountable for defining, building, and operationalising the TOC's agentic capability so that Bupa can move from fragmented, reactive monitoring to governed, event-driven, AI-enabled operations. The role owns the strategy and execution required to create a safe, resilient, and scalable operational capability that improves decision speed, strengthens 24×7 resilience, reduces manual toil, and enables proactive and autonomous action where it is safe and appropriate to do so. role bridges AI engineering, Technology Operations, product management, observability, data, and governance. It is responsible for ensuring that agentic capability is designed around operational reality: high signal volumes, real customer impact, strict control boundaries, major incident conditions, cross-team hand-offs, and the need for reliable human override.

The candidate will have responsibilities across the following functions:

AI Engineering, Agentic Capability and Product Leadership:

• Lead the design, build, and operationalisation 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 judgment 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 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, 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 organisational change.

Preferred / 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.

Autonomy, Scope, Level, and Scale of Role:

• Accountable for a critical specialist capability at the intersection of Technology Operations, AI, and automation.
• Directs interconnected streams of work spanning operating model, product delivery, engineering, control design, data, and service adoption.
• Provides authoritative advice to senior stakeholders on AI-enabled operations, operational risk, technology choices, and adoption priorities.
• Operates with substantial autonomy within agreed outcomes and enterprise policy boundaries, using professional judgement to balance pace, safety, cost, and value.
• Owns delivery against major operational outcomes and is expected to establish sustainable support, governance, and improvement mechanisms rather than short-term point solutions.

Skills

AI, AIOps, GCP, Google Cloud, ServiceNow, automation, monitoring, observability, telemetry