AI Platform Engineer

The AI Platforms Engineer will support, build, and operate the enterprise AI platform foundation, enabling AI services, AI-enabled solutions, and AI agents to run reliably and securely on cloud. The role spans implementation, integration, productionisation, and day-to-day operation of AI platforms, with a strong focus on building the underlying cloud infrastructure for AI and agents.This is a hands-on role that both supports AI adoption and builds the cloud infrastructure behind it, provisioning the compute, accelerated (GPU) compute, networking, storage, and platform services required to run AI workloads and agentic solutions. The role works closely with Cloud Platforms, Data Platforms, Architecture, Security, Infrastructure, and business teams.It helps ensure AI platforms and AI agents are reliable, secure, well-governed, scalable, and ready for enterprise adoption, covering corporate AI use cases, centralized AI productionisation, and function-specific agent building.Key ResponsibilitiesBuild, configure, and operate the cloud infrastructure that underpins enterprise AI platforms and agents, including compute, accelerated (GPU) compute, networking, storage, containers, and platform services.Provision and manage the runtime, hosting, and deployment environments required to run AI models, services, and agents on cloud.Support the implementation, configuration, administration, and operation of enterprise AI platforms.Support Azure AI Services, Azure OpenAI, Azure AI Foundry, MCP gateways, AI agents, APIs, connectors, and related integration services.Build and support AI agents that connect securely to approved enterprise data sources, tools, APIs, and systems.Support and enable AI developer and coding assistants across engineering teams, including Claude Code, OpenAI Codex, GitHub Copilot, and Cursor.Work with business teams to identify, assess, prioritise, and onboard AI use cases.Support corporate AI use cases, productivity assistants, knowledge assistants, workflow automation, and function-specific AI agents.Support centralised productionisation of AI use cases, including testing, release readiness, monitoring, documentation, and operational handover.Work with Cloud and Data Platforms teams to enable secure, scalable, and governed AI solutions.Support AI platform evaluations, model and tool assessments, proofs-of-concept, and roadmap inputs.Monitor AI platform and infrastructure health, usage, performance, cost, access, and operational issues.Troubleshoot AI platforms, infrastructure, integration, data-access, and service-related issues.Maintain technical documentation, operational procedures, support runbooks, and AI solution records.Ensure AI solutions and infrastructure follow enterprise security, data governance, responsible AI, and operational standards.Contribute to continuous improvement, automation, reusable patterns, and platform enhancement initiatives.Required Qualifications & ExperienceBachelor’s degree in computer science, Information Technology, Engineering, AI, Data Science, or a related discipline.6–8 years of hands-on experience in cloud, data, AI, integration, automation, or enterprise platform roles.Practical experience with Microsoft Azure and AI-related cloud services, including building or operating cloud infrastructure.Experience provisioning cloud infrastructure for workloads, including compute, networking, storage, containers, and platform services.Experience supporting APIs, integrations, connectors, and enterprise connectivity frameworks.Understanding of AI platforms, AI agents, prompt-based solutions, and enterprise AI use cases.Working understanding of data access, identity, permissions, security controls, and governance.Strong analytical, troubleshooting, and problem-solving skills.Ability to work effectively with technical teams and business stakeholders.Preferred SkillsExperience with Azure AI Services, Azure OpenAI, Azure AI Foundry, Copilot, or similar enterprise AI platforms.Experience with MCP gateways, AI agents, tool calling, workflow automation, or agentic platforms.Familiarity with AI coding assistants and agentic developer tools such as Claude Code, OpenAI Codex, GitHub Copilot, and Cursor.Experience building AI infrastructure on cloud, including accelerated (GPU) compute, containers / Kubernetes, networking, and model hosting.Exposure to Snowflake, Power BI, Azure Data Factory, or enterprise data platforms.Experience productionising AI, automation, or integration solutions.Exposure to Infrastructure as Code, automation, and CI/CD for AI platforms.Understanding of responsible AI, AI governance, model evaluation, and AI risk controls.Experience creating technical documentation, runbooks, reusable implementation patterns, and user enablement material.Preferred CertificationsMicrosoft Azure AI Engineer certification.Microsoft Azure (infrastructure or administrator) certifications.AI, data, cloud, integration, automation, or security-related certifications.Snowflake or data platform certification is an advantage.Key Success MeasuresA reliable, secure, and scalable cloud infrastructure foundation for AI platforms and agents.Successful onboarding and support of enterprise AI use cases.Reliable, secure, and governed operation of AI platforms and AI agents.Effective productionisation of AI solutions from prototype to operational service.Improved AI platform adoption, usability, and business value.Timely resolution of AI platforms, infrastructure, integration, and data-access issues.Demonstrated compliance with enterprise AI governance, security, and operational standards.