Infrastructure & AI Cloud Solution Architect

Job Overview

We are undertaking an AI Transformation Assessment to define the strategic, financial, and operational foundations for becoming an AI-Empowered media organisation.

The assessment will evaluate the current state, identify high-value AI use cases across departments, quantify the investment and ROI, and deliver a Business & Investment Case supported by a prioritised Implementation Roadmap.

A multidisciplinary team — including business, financial, media, and technical experts — will collaborate with stakeholders to produce actionable insights, establish readiness for AI adoption, and guide the transition toward data-driven, automated, and scalable operations.

We are seeking an Infrastructure & AI Cloud Solution Architect to design, assess, and recommend the scalable, secure, and cost-efficient infrastructure architecture required to support AI workloads across Customer’s cloud and on-prem environments. The role ensures the AI Transformation Assessment captures realistic technical dependencies, cost implications, and readiness gaps—bridging strategy and implementation planning.

It will work with the Customer Technology Office to align and re-enforce architecture standards for integration of third-party services; optimising performance and cost-efficiency for the chosen AI platforms and technologies.

• A validated infrastructure assessment report outlining current capabilities, gaps, and readiness for AI workloads.
• A proposed target architecture design and roadmap supporting the AI Transformation Plan.
• Cost and effort inputs feeding into the Business & Investment Case.
• Documented recommendations and risks for infrastructure scalability, resilience, and vendor integrations.

Design and maintain complex financial models capturing cost structures, effort estimates, productivity gains, and long-term ROI across multiple departments.

Conduct baseline financial assessments of current operations, identifying cost inefficiencies and potential automation savings.

Develop scenario projections (Conservative, Base, Ambitious) that illustrate investment pathways, payback periods, and breakeven points.

Quantify tangible and intangible benefits, including efficiency gains, risk reduction, workforce redeployment, and audience growth impact.

Consolidate all quantitative findings into a robust and defensible Business & Investment Case, supported by sensitivity and risk analyses.

Partner with the AI Project Manager, Business Analysts, Technical SMEs, and Department Heads to validate assumptions and align financial outputs with operational realities.

Prepare executive-ready dashboards and summaries, translating analytical results into actionable insights for decision-makers.

Support Steering Committee presentations by articulating key financial narratives, trade-offs, and recommendations.

• 10+ years of experience in infrastructure or cloud architecture.
• Hands-on experience working in data, or digital transformation projects.
• Demonstrated strong expertise in designing AI / ML-ready environments, including Cloud GPU / compute orchestration, data pipelines, and storage optimisation.
• Strong background in hybrid cloud architecture (cloud + on-prem integration) and API-driven ecosystems.
• Proven ability to translate business and AI requirements into scalable technical designs and cost-efficient infrastructure solutions.
• Excellent understanding of security, compliance, and data governance in enterprise AI deployments.
• Exceptional communication skills, with the ability to collaborate across technical and non-technical stakeholders.

• Active Cloud Certifications in Microsoft Azure and AWS (Professional-Level)
• Hands on AWS Cloud Design and implementation.
• Natural Language Processing (NLP): process queries, and generate human-like text responses—Including Sentiment Analysis applications
• Experience with MLOps, Data Ops, or automation frameworks is highly desirable.
• Generative AI Expertise:
  Experience designing and architecting AI Infrastructure with LLMs, text-to-image / video, and creative AI tools for media workflows.
• Data & MLOps:
  Strong in ETL pipelines, real-time streaming, and ML lifecycle management architecture design.
• Media Infrastructure Knowledge:
  Understanding of MAM / DAM systems, metadata, transcoding, and integration of streaming protocols is a plus.
• Cloud & GPU Architecture:
  Skilled in multi-cloud (AWS / Azure / GCP) and GPU-based AI infrastructure design within Air-Gapped sovereign environments.