Data & AI Reliability Engineering Consultant/Architect

Responsibilities

• Drive the technical side of presales: qualify requests, facilitate client workshops, define scope, assumptions and estimates
• Conduct discovery and maturity assessments of a client's observability, data reliability and AI operations; deliver findings and a prioritised roadmap
• Author the solution section 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
• Establish 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 outcomes of the change
• Serve as the trusted advisor for client engineering leads, SDMs and directors throughout the engagement
• Lead the first phase of delivery after a won deal, then transition to the engineering team while remaining accountable for the solution
• Review engineers' work, set technical standards (alert-as-code, dashboards-as-code, IaC) and unblock decisions
• Convert project experience into reusable assets: offerings, accelerators, assessment frameworks, reference architectures

Languages