Job description

Scale AI's Delivery Teams build and launch new AI use cases for strategic public sector partners, and this role takes over from there, owning the long-term technical health, performance, and stability of what gets deployed. You will bridge software engineering, MLOps, and client governance, managing tiered SLAs, tracking model drift, and executing maintenance protocols that protect both system integrity and operational margins. Handover Gate reviews sit at the front of the work, acting as the technical gatekeeper during the formal transition from Delivery to Maintenance and running deep-dive reviews of code, prompts, and architecture against maintainability and documentation standards. SLA and incident management follow, owning technical response and resolution targets across service tiers that range from Business-Hours Essential up to round-the-clock, highest-priority support, and leading Incident Governance, Root Cause Analysis, and P1/P2 mitigations within strict active support windows. Production model performance, latency, and data drift get monitored continuously, with prompt configuration repositories managed to hold behavioral consistency steady and regression testing run whenever LLM providers update their endpoints. Incoming client requests get assessed against the boundary between Routine Maintenance and System Evolution, with comparative benchmarking run on new AI models as needed. Operational toil gets engineered out through self-healing data pipelines, automated RAG indexing syncs, and telemetry tooling, while upstream Delivery teams get influenced toward architectural patterns that simplify ongoing maintenance. Government and enterprise IT leads rely on you as the senior technical point of contact, translating concepts like data drift, prompt versioning, and API deprecation into business impacts they can act on.