The Role
Own the enterprise AI platform operating model, standards, governance, security, deployment patterns, and transition from experimentation to production. Define reusable LLM, RAG, evaluation, gateway, and application-integration patterns; guide MLOps engineers and external partners; establish cost and usage visibility; and coordinate architecture, cloud, security, and data teams. Report roadmap, risks, adoption, and production readiness to senior and CIO-level stakeholders.
Summary Generated by Built In
Job Summary & Responsibilities
- 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.
Skills Required
- 7+ years of experience in platform engineering, DevOps, cloud engineering, ML engineering, or enterprise software operations, including technical leadership or architecture responsibility.
- Experience moving workloads from experimentation into stable, governed production operations at enterprise scale.
- Experience setting technical direction for a small engineering team 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, including reviewing technical designs and code-level decisions.
- Understanding of Docker, Kubernetes, and cloud AI/ML services on Azure or AWS.
- Working knowledge of MLOps concepts, including 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.
- Ability to communicate technical trade-offs clearly to architects, managers, and CIO-level stakeholders.
- Fluency in spoken and written English.
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
For over a half-century, Advantest has been designing innovative electronic measuring equipment and semiconductor test systems essential to the development and manufacture of advanced computer and telecommunications products. On April 1, 2012, Advantest completed its integration of Verigy Ltd. Additional Information about Advantest can be found at www.advantest.com.
