AI Operations / AI Platform Operations / AI Platform Administrator

Job Description: 

AI Operations Lead

 

Reporting to: Head of IT	Author: James Kelly
Location: [Remote / Hybrid]	Date Written/Updated: 7 September 2026
Job Level: Operational Lead	Starting Salary: 60k

 

Role purpose

The AI Operations Lead exists to drive the reliable day-to-day operation, governance, and business adoption of the organisation's enterprise AI platforms. Rather than acting as a software development post, this role serves as the central operational hub for AI gateway administration, workflow automation, and Model Context Protocol (MCP) integrations. The position balances operational agility with strict governance by establishing essential controls, monitoring platform usage, managing vendor relationships, and safeguarding data security. Ultimately, the role bridges technical teams, risk functions, and end-users to ensure AI technologies deliver safe, cost-effective, and measurable value across the enterprise.

Dimensions

• Financials: Oversight and optimisation of enterprise AI token consumption, API spend, and usage budgets across external providers (e.g., Anthropic and Amazon Bedrock); directly manages platform access controls to prevent cost overruns. 

• Team: Individual contributor role with no direct line-management responsibilities; acts as a functional lead for process automation and platform access management.

• Scope: Organisation-wide reach; supports all internal business units, technical teams, and risk functions using approved enterprise AI gateways, tools, and automated workflows.

Principal Accountabilities

AI Gateway & Platform Administration

• Steer the daily operational management of the enterprise AI gateway (e.g., LiteLLM), overseeing model routing, rate limits, fallback pathways, and service priorities.

• Engineer model catalogues, user groups, and fine-grained access permissions for third-party models, including Anthropic and Amazon Bedrock.

• Monitor availability, error rates, response latency, and token consumption to maintain robust service performance.

Workflow Automation & Process Optimisation

• Construct and maintain low-code workflow automations (e.g., via n8n) that integrate enterprise applications with approved AI services.

• Collaborate with business process owners to translate manual, repetitive tasks into secure, auditable automated workflows with human-in-the-loop validation.

• Catalogue all operational workflows, maintaining an accurate inventory of dependencies, error logs, and support arrangements.

AI Agent & MCP Integration Management

• Configure, test, and deploy AI agents and Model Context Protocol (MCP) connections to allow safe interactions between models and enterprise data sources.

• Enforce least-privilege access principles across agent knowledge bases, tools, and execution boundaries.

• Track production agent behaviour to prevent unintended actions, hallucinations, or security exceptions, escalating complex development requirements to platform engineering.

Governance, Risk & Compliance Support

• Enforce AI governance policies across all active models, agents, workflows, and integrations, conducting regular reviews against security and privacy standards.

• Maintain comprehensive audit trails for user access, configuration updates, and model interactions to support internal and regulatory audits.

• Assist legal, risk, and privacy teams with AI impact assessments, vendor risk reviews, and data residency compliance checks.

Service Operations & Support

• Act as the primary point of contact for platform access requests, technical queries, operational incidents, and change requests.

• Author and update operational runbooks, technical documentation, user guides, and self-service knowledge base articles.

• Manage incident escalation pathways and liaise with external platform vendors to resolve outages or service degradation swiftly.

Monitoring, Reporting & Cost Control

• Produce analytical dashboards detailing service metrics, user adoption trends, usage spikes, and total cost of ownership across departments.

• Implement usage quotas, alert thresholds, and strict budget caps to avoid unexpected expenditure or policy violations.

• Identify inefficiencies in platform usage and recommend cost-optimisation strategies without compromising operational reliability.

User Enablement & Community Engagement

• Deliver onboarding sessions, practical demonstrations, and guidance to upskill business teams on approved AI tools.

• Promote best practices regarding prompt structure, responsible AI usage, and process automation across the wider business.

• Establish and champion an internal Community of Practice to encourage knowledge sharing, reuse of approved agents, and collaborative feedback.

Key success indicators

• High service availability and prompt, SLA-aligned resolution of platform incidents and user access requests.

• 100% compliance across active models, agents, and workflows regarding documented business owners, risk assessments, and governance approvals.

• Effective management of platform costs within defined budgetary parameters through usage tracking and quota management.

• Measurable reduction in manual workload through the successful implementation of secure, controlled workflow automations.

• Consistent user adoption growth and high stakeholder satisfaction across trained business units.

Required experience and profile

Essential

• Demonstrated experience in technology operations, application support, platform administration, or service management.

• Hands-on experience administering AI platforms, AI gateways (e.g., LiteLLM), or SaaS automation tools (e.g., n8n, Make, or Power Automate).

• Solid foundational understanding of REST APIs, webhooks, JSON data structures, role-based access control (RBAC), and secret management.

• Familiarity with cloud-hosted AI providers (e.g., Amazon Bedrock and Anthropic) and general generative AI concepts, including tokens, context windows, temperature, and embeddings.

• Proven experience managing incident response, system documentation, and operational controls within structured IT service environments.

• Strong written and spoken communication skills, with an ability to translate technical concepts for business stakeholders.

Desirable

• Experience working within an ITIL-aligned service framework or highly regulated, security-sensitive environment.

• Familiarity with Model Context Protocol (MCP), LLM observability tools (e.g., Langfuse or Datadog), or AWS infrastructure.

• Basic scripting capability in Python or JavaScript for troubleshooting and minor data-mapping tasks.

• Awareness of AI governance frameworks (e.g., ISO/IEC 42001 and NIST AI RMF) or information security standards (ISO 27001 and SOC 2).

• Relevant certifications in AWS Cloud, or AI Governance.

Key Relationships

Relationship (Stakeholder)	Purpose
Head of IT	Direct line reporting; alignment on operational priorities, strategy, and platform roadmaps.
Business Unit Process Owners	Identifying manual workflows, gathering requirements, configuring automations, and providing platform support.
Software Engineering & Platform Teams	Escalating complex integration tasks, back-end infrastructure requirements, or custom development.
Information Security, Legal & Risk Teams	Ensuring AI usage complies with data protection, privacy guidelines, audit standards, and risk controls.
External AI Vendors & Service Providers	Managing service availability, resolving platform bugs, monitoring performance, and overseeing SLA compliance.