AI Platform Engineer

Strong understanding of AI Platform Engineering, MLOps, or AI infrastructure management principles. • Hands-on experience with Terraform and Infrastructure as Code implementation across enterprise environments. • Proficiency in Python scripting, automation development, and platform engineering activities. • Knowledge of prompt engineering techniques, LLM evaluation methodologies, and AI model lifecycle management. • Experience implementing Security as Code practices, security automation, and compliance controls. • Familiarity with cloud platforms, DevOps tooling, CI/CD pipelines, and infrastructure automation frameworks. • Strong problem-solving, analytical thinking, stakeholder management, and troubleshooting capabilities. ________________________________________ Technical Certification Need to Have and Good to Have Need to Have • HashiCorp Certified: Terraform Associate. • Microsoft Certified: Azure Administrator Associate (AZ-104) or equivalent cloud administration certification. • Python programming certification or demonstrable enterprise-level Python development experience. • Security fundamentals certification such as CompTIA Security+ or equivalent. • Proven experience in Infrastructure as Code, platform engineering, and automation disciplines. Good to Have • Microsoft Certified: Azure AI Engineer Associate (AI-102). • Microsoft Certified: Azure DevOps Engineer Expert (AZ-400). • Certified Kubernetes Application Developer (CKAD) or Certified Kubernetes Administrator (CKA). • Certified Information Systems Security Professional (CISSP) or Certified Cloud Security Professional (CCSP). • MLOps, Generative AI, Prompt Engineering, or Large Language Model specialization certifications. • Cloud provider certifications from AWS or Google Cloud related to AI, DevOps, or infrastructure. • Certifications in Security as Code, DevSecOps, or cloud-native security practices

Experience working with enterprise AI platforms, Generative AI services, or foundation model ecosystems is highly desirable. • Exposure to AI governance, responsible AI practices, and model risk management frameworks is preferred. • Ability to collaborate effectively across UK and Offshore teams in a global delivery environment. • Strong communication skills with the ability to explain technical concepts to technical and business stakeholders. • Experience supporting large-scale platform transformation, automation, and modernization initiatives. • Passion for innovation, emerging AI technologies, and continuous learning. • Commitment to building secure, scalable, resilient, and compliant AI platforms that deliver measurable business value.

Job Description - AI Platform Engineer Function: CSO Role: AI Platform Engineer Hiring Managers: Louise Furniss / Julie Wilson Grade: E / F Location: UK / Offshore Open Positions: 2 Target Demand: 10 Resources ________________________________________ Job Summary • Join the CSO team to design, build, and operate enterprise-scale AI platforms that enable secure and scalable AI adoption. • Develop platform capabilities supporting Generative AI, prompt engineering, model evaluation, and AI service delivery. • Contribute to the implementation of Infrastructure as Code (IaC) and Security as Code practices across AI environments. • Collaborate with security, engineering, data, and platform teams to deliver reliable and compliant AI solutions. • Support the development of AI platform standards, governance controls, and operational best practices. • Drive automation, scalability, and efficiency across the AI technology stack using modern DevOps methodologies. • Enable business teams and developers to safely consume AI capabilities through robust platform engineering.

Design, build, and maintain AI platform infrastructure supporting AI and Generative AI workloads. • Implement Infrastructure as Code solutions using Terraform, Python, and cloud-native technologies. • Develop and maintain automation frameworks for AI platform provisioning, deployment, and management. • Support prompt engineering initiatives, model testing, validation, and evaluation frameworks. • Embed Security as Code controls to ensure secure AI platform deployment and operations. • Monitor platform health, reliability, performance, and operational metrics while driving continuous improvements. • Collaborate with stakeholders to define platform requirements and deliver scalable enterprise AI capabilities.