Job Description

Job Summary

Job Title: AI Platform Engineer / Architect

Job Summary

We are looking for experienced AI Platform Engineers / Architects with deep expertise in designing and building enterprise-grade AI platforms on Cloud (Azure) environments. The role focuses on establishing scalable, secure, and governed AI/GenAI ecosystems—covering model usage, agentic systems, platform engineering, and operational excellence. The ideal candidate will drive platform standardization, enable self-service AI capabilities, and ensure compliance with emerging AI regulations.

Key Responsibilities

Key Responsibilities

• Define and implement zero-trust identity architecture for AI platforms (Identity & RBAC)
• Architect and establish enterprise landing zones within Azure environments
• Develop and govern AI/LLM model usage strategy and lifecycle frameworks
• Standardize and implement prompt engineering frameworks and best practices
• Design enterprise knowledge architectures leveraging RAG and vector search
• Develop and scale agentic AI systems, including enterprise-grade agent frameworks
• Build and manage platform engineering capabilities, including:
• Infrastructure as Code (IaC)
• Golden paths and standardized productized platforms
• Self-service platform pipelines (CI/CD)
• Establish GenAIOps / MLOps lifecycle management frameworks for model deployment and monitoring
• Define and enforce AI governance and compliance frameworks, aligned with regulations such as the EU AI Act
• Drive observability strategy, including monitoring, logging, and FinOps alignment
• Define and enforce engineering standards, including best practices in Python and software development

Skill Requirements

Must-Have Skills

• Strong expertise in Cloud & Azure Foundations, including Identity & Access Management (IAM), RBAC, networking, and enterprise landing zone design
• Deep knowledge of AI / LLM ecosystems, including model strategy, lifecycle management, and governance frameworks
• Hands-on experience with prompt engineering frameworks and optimization techniques
• Proven experience in RAG-based architectures, including vector search and enterprise knowledge systems
• Expertise in designing and implementing agentic AI systems and enterprise agent frameworks
• Strong capabilities in platform engineering, including Infrastructure as Code (IaC), CI/CD pipelines, and self-service platform enablement
• Experience with MLOps / GenAIOps frameworks, including model lifecycle management, deployment, and governance
• Solid understanding of AI security, governance, and compliance, including regulatory frameworks such as the EU AI Act
• Experience in observability and operational excellence, including monitoring, logging, and FinOps practices
• Strong programming skills in Python and adherence to engineering best practices

Other Requirements

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