AI Platform Engineer (m/f/d)
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- Own the target operating model for the CIT AI platform, including governed exploration, model access, deployment patterns, operational ownership and handover between teams and external partners.
- Define reusable platform patterns and standards for LLM APIs, RAG components, evaluation pipelines, AI gateway integration and business application integration.
- Set technical direction and priorities for MLOps Engineer(s), review key build decisions and ensure implementation choices remain aligned with platform standards.
- Own the transition path from sandbox or PoC environments into production-ready architectures, including support model, lifecycle ownership and operational readiness criteria.
- Define cost transparency and usage visibility for AI platform consumption, including token, cost and usage reporting patterns.
- Coordinate and steer nearshore, system integration and cloud implementation partners while retaining internal accountability for platform outcomes.
- Own platform decisions, security assumptions, interface documentation, architecture decisions and handover requirements at governance level.
- Act as the primary contact for architecture, security, governance, data engineering, cloud platform and application teams on AI platform matters.
- Report platform roadmap, risks, decisions, adoption progress and production-readiness status to CIO-level and senior stakeholders.