Data & AI Reliability Engineering Consultant/Architect

Responsibilities

• Lead the technical side of presales: qualify the request, run client workshops, define scope, assumptions and estimates
• Run discovery and maturity assessments of a client's observability, data reliability and AI operations; deliver findings and a prioritised roadmap
• Write the solution part of proposals and RFP responses; present and defend it to client technical and business stakeholders
• Design target architectures for observability and reliability of data platforms and AI/LLM workloads, including tool selection and migration paths
• Define SLOs, SLIs and error budgets for data products and AI services; translate them into alerting, incident and governance processes
• Build the business case: cost of incidents, tooling cost optimisation, expected effect of the change
• Act as the trusted advisor for client engineering leads, SDMs and directors during the engagement
• Lead the first phase of delivery after a won deal, then hand over to the engineering team while staying accountable for the solution
• Review the work of engineers, set technical standards (alert-as-code, dashboards-as-code, IaC), unblock decisions
• Turn project experience into reusable assets: offerings, accelerators, assessment frameworks, reference architectures

Languages