Lead AI Operations Engineer

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Job Function: Technology Product & Platform Management Job Sub Function: Technical Product Management Job Category: Scientific/Technology All Job Posting Locations: Lisbon, Portugal, Madrid, Spain, Milano, Italy

Job Description
We are recruiting for a Lead AI Operations Engineer based in Milan
- Italy ; Madrid ; Spain or Lisbon ; PortugalThe AI Operations Engineer is responsible for shaping, designing, implementing, and continuously improving the enterprise capabilities required to operate AI applications and AI agents safely, reliably, transparently, and cost-effectively at scale. The role combines hands‑on AI platform engineering with Site Reliability Engineering, DevSecOps, LLMOps, AgentOps, FinOps, security, and compliance practices. The AI Operations Engineer builds reusable operational capabilities across observability, runtime controls, cost management, auditability, incident response, and production support. The role works closely with the Agent Factory, AI Engineering, Data Platforms, Cloud Infrastructure, Cybersecurity, Privacy, Risk, Quality, and Responsible AI stakeholders. The role does not own the end‑to‑end lifecycle management of agents or AI products. Agent design, development, functional evaluation, release content, product evolution, and retirement decisions remain with the Agent Factory and the relevant AI product teams. The AI Operations Engineer provides the shared operational platform, telemetry, controls, and guardrails that enable those teams to run AI solutions in production.

AI Observability & Production Reliability: Design and implement end‑to‑end observability for AI applications and agents, including prompts, responses, model calls, tool calls, retrieval steps, decision paths, latency, failures, token consumption, and session context. Establish common telemetry and distributed tracing across agent workflows, APIs, data services, vector stores, model endpoints, and external tools. Build operational dashboards and alerts covering availability, latency, errors, reliability, quality signals, policy violations, consumption, and service health. Define service‑level indicators, service‑level objectives, error budgets, alert thresholds, and operational readiness criteria for production AI services. Enable trace‑based debugging, incident reconstruction, and controlled session replay while protecting confidential or sensitive information in logs. Monitor retrieval quality, data freshness, model and prompt regressions, anomalous agent loops, degraded tool performance, and unexpected runtime behavior. Lead technical root‑cause analysis for AI platform and runtime incidents and convert findings into preventive controls, automation, and engineering improvements.

LLMOps & AgentOps Platform Enablement Engineer: Platform Enablement Engineer reusable pipelines, templates, and controls for configuration, prompt, model, and agent‑component versioning across environments. Implement automated technical gates for deployment readiness, including integration tests, regression checks, operational validation, security checks, and observability coverage. Enable controlled rollout patterns such as canary releases, feature flags, model or provider routing, fallback strategies, and technical rollback mechanisms. Provide common operational tooling that supports multiple models, frameworks, clouds, and agent patterns without creating a separate operating process for each solution. Integrate functional evaluation signals supplied by the Agent Factory or AI product teams into deployment gates and runtime monitoring, while functional quality ownership remains with those teams. Maintain reusable runbooks, reference implementations, engineering standards, and paved‑road patterns for production operation and support.

AI FinOps & Consumption Efficiency: Create transparent metering, allocation, and showback capabilities by product, agent, workflow, model, environment, and business unit where the required identifiers are available. Monitor token consumption, model utilization, repeated or runaway loops, retrieval overhead, infrastructure usage, and cost