Job Description

• 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.

Qualifications

• 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.