AI Platform Engineer (m/f/d)

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.

7+ years of experience in platform engineering, DevOps, cloud engineering, ML engineering or enterprise software operations, including technical leadership or architecture responsibility. Track record of moving workloads from experimentation into stable, governed production operations at enterprise scale. Experience setting technical direction for a small engineering team and/or steering external delivery partners while retaining internal accountability. Strong background in Python-based engineering, CI/CD, Git-based workflows and modern software delivery practices, with the ability to review technical designs and code-level decisions. Solid understanding of Docker, Kubernetes and cloud AI/ML services on Azure or AWS. Working knowledge of MLOps concepts such as model registries, evaluation pipelines, drift monitoring, retraining workflows and production observability. Understanding of enterprise security expectations, including identity, network isolation, secrets management, API access control and data protection implications. Ability to communicate technical trade-offs clearly to architects, managers and CIO-level stakeholders. Fluency in English, spoken and written.