About Us
Skybound Wealth Management is a global financial advisory company with employees across the UK, USA, Switzerland, Cyprus, Spain and UAE. We provide tailored financial advice to international clients, supported by expert teams across wealth planning, compliance and operations.
Role Overview
We are seeking an experienced and technically capable AI Platform & LLMOps Engineer to take ownership of the reliability, performance and economics of our models, prompts, agents and AI services in production.
The successful candidate will be responsible for the operational layer between model providers and the applications that consume them. This will include deployment, observability, evaluation pipelines, provider integrations, routing, quotas, release controls, cost optimisation and incident response.
Working closely with our software engineering, data, cybersecurity and product teams, the AI Platform & LLMOps Engineer will ensure our AI services are observable, resilient, repeatable, secure and cost-controlled.
This role requires someone who can operate and improve the underlying AI platform while also understanding how AI services are integrated into existing production applications and business workflows.
Key Responsibilities
AI Platform Operations
Operate and maintain the Group’s model gateway and AI provider integrations.
Manage authentication, model routing, fallback processes, rate limits, quotas and provider controls.
Maintain controls relating to model and provider versions, deprecations and service changes.
Ensure AI services are resilient, scalable and capable of supporting production applications and workflows.
Work closely with software engineering teams to integrate AI services into existing and new applications.
Monitoring
Build and maintain dashboards and alerts covering token usage, cost, latency, error rates and service availability.
Monitor quality and safety evaluations, tool failures and AI usage by person, team, product and use case.
Implement distributed tracing, metrics and logging using OpenTelemetry or equivalent technologies.
Define and maintain service-level objectives, operational thresholds and escalation processes.
Identify emerging performance, reliability or cost issues before they affect users or business operations.
Release Management
Build prompt, agent and model release pipelines with appropriate version control and approval processes.
Implement automated testing and evaluation as part of the AI release lifecycle.
Manage canary deployments, controlled releases and rollback processes.
Define and measure model accuracy, service quality and business value, rather than focusing solely on infrastructure performance and cost.
Establish evaluation frameworks that measure whether AI services successfully complete the intended task, case, document or workflow.
Monitor model performance and identify potential drift or deterioration in output quality.
Cost Management & Optimisation
Develop clear unit economics for AI services, progressing from cost per token to cost per successful task, case, document or workflow outcome.
Monitor and report AI consumption and expenditure across teams, products and use cases.
Optimise spend through model routing, caching, batching, prompt and context reduction, model selection and capacity planning.
Balance cost, speed, accuracy and quality requirements when selecting and configuring AI services.
Explain cost and quality trade-offs clearly to engineering, finance and executive stakeholders.
Resilience & Incident Management
Conduct load, resilience and red-team testing across AI services and supporting infrastructure.
Develop and maintain operational playbooks for provider outages, model drift, data leakage, prompt injection and unexpected cost increases.
Investigate and respond to incidents affecting AI services.
Maintain incident records, post-incident reviews and remediation plans.
Assess provider and model changes to identify operational, security and business risks.
Cloud Infrastructure & DevOps
Manage AI infrastructure through infrastructure-as-code and automated deployment practices.
Build and maintain CI/CD pipelines, containers, serverless compute and environment separation.
Manage secrets, authentication, networking, telemetry and access controls.
Support the operation of cloud-native AI services, applications and integrations.
Maintain clear technical documentation covering architecture, deployment processes, dependencies and operational procedures.
Security & Governance
Embed DevSecOps and secure software development lifecycle practices across the AI platform.
Conduct threat modelling and support security reviews for AI services and application integrations.
Implement automated security testing within CI/CD pipelines.
Maintain dependency, vulnerability and software supply-chain controls.
Ensure secrets, credentials and sensitive information are managed securely.
Support auditability, data protection and regulatory requirements relating to financial and client information.
Maintain appropriate audit evidence, operational records and provider assessments.
Requirements
Essential
5+ years’ experience in platform engineering, SRE, DevOps, MLOps, cloud engineering or a related role, including responsibility for production services and incident response.
Hands-on experience operating LLM or machine-learning services in production, with a strong understanding of token usage, context limits, latency and probabilistic failure.
Strong Python or scripting capability, with practical experience of CI/CD, infrastructure-as-code, containers and cloud-native services.
Experience implementing observability using tools such as OpenTelemetry, including tracing, metrics, logging, dashboards and alerting.
Strong knowledge of cloud and application security, including identity, networking, secrets management, encryption, audit logging and vulnerability management.
Practical DevSecOps experience, including secure development practices, threat modelling, automated security testing and software supply-chain controls.
Experience integrating AI or cloud services into production applications and working closely with software engineering and product teams.
Ability to measure model quality, service performance, cost and business outcomes, and communicate technical trade-offs clearly to a range of stakeholders.
Desirable
Practical experience with Microsoft Azure, including Azure OpenAI, Azure AI services, App Services, Functions, Key Vault, Application Insights or Azure DevOps.
Experience operating AI or technology services within financial services or another regulated environment.
Familiarity with recognised security frameworks such as the OWASP Application Security Verification Standard.
Experience designing model gateways, multi-provider routing, fallback processes or cost-optimised model selection.
Experience developing AI evaluation, red-team or model-testing frameworks.
You Will Benefit From
A competitive salary relevant to experience.
The opportunity to take ownership of the operational platform supporting AI across a growing international business.
Exposure to AI projects spanning client services, financial advice, compliance, operations and internal productivity.
The opportunity to shape the Group’s standards for AI reliability, security, cost management and evaluation.
The chance to work closely with software engineering, data, cybersecurity, finance and executive stakeholders.
Full support to complete relevant industry certifications and continue developing your AI, cloud and platform engineering career.
Skybound Wealth Management is committed to fostering a diverse and inclusive workplace. We welcome applications from all qualified candidates regardless of background.