A leading AI platform engineering organization in Abu Dhabi, UAE is seeking an experienced Agent Runtime Platform Engineer to design and build the enterprise runtime foundation for AI agents and agentic applications. This senior platform engineering role is focused on creating secure, reusable, and self-service runtime capabilities that enable development teams to deploy AI agents consistently and reliably across cloud-native environments. The role requires 7+ years of relevant platform engineering experience with deep, hands-on expertise in AKS, Azure Container Apps, Kubernetes, Terraform, GitHub Actions, Workload Identity, API Gateways, Networking, Ingress, Secrets Management, and Private Connectivity, combined with working knowledge of AI agent architectures, MCP servers, and API/service contracts. Apply directly to careers@d4insight.com.
About the Role — Agent Runtime Platform Engineer, Abu Dhabi UAE 2026
Role Type: Full-Time Senior Platform Engineering | Enterprise AI Agent Runtime Infrastructure | Abu Dhabi, UAE
Platform Scope: Design and build enterprise runtime foundation for AI agents and agentic applications — AKS · Azure Container Apps · Kubernetes · MCP servers · API Gateways · Private Connectivity
Engineering Focus: Secure, reusable, self-service runtime capabilities · Agent manifests · Runtime configurations · Tool definitions · Paved-road deployment templates · SDKs · Reference implementations
Cross-Team Collaboration: Work closely with AI Platform · AI Ops · Cybersecurity · Architecture · and Application teams throughout delivery
Application: Send your CV directly to careers@d4insight.com — apply now while the role is actively open
Why This Agent Runtime Platform Engineer Role in Abu Dhabi Is a Career-Defining Opportunity in 2026
Frontier AI Infrastructure: Enterprise AI agent runtime platform engineering — building the production infrastructure that makes agentic AI applications reliable, secure, and scalable for real enterprise workloads — is one of the newest, rarest, and most rapidly valued platform engineering specialisms in the global technology market in 2026
MCP & Agentic AI at Scale: Designing runtime patterns for MCP servers, agentic applications, and AI agent APIs at enterprise scale places this role at the absolute frontier of enterprise AI infrastructure engineering — building the kind of production-grade, cloud-native agentic AI platform that very few engineers in the world have yet had the opportunity to architect and deliver
Abu Dhabi AI Investment: Abu Dhabi’s extraordinary investment in AI infrastructure, enterprise AI adoption, and AI platform capability development makes it one of the most professionally dynamic and financially rewarding environments in the world for senior platform engineers specializing in AI agent infrastructure
Tax-Free Abu Dhabi: Highly competitive senior platform engineering compensation — zero personal income tax in Abu Dhabi, UAE
Position Overview — Build the Enterprise AI Agent Runtime
This Agent Runtime Platform Engineer role in Abu Dhabi is a full-time, senior platform engineering position focused on designing and building the enterprise runtime foundation that makes AI agents and agentic applications production-ready, consistently deployable, and reliably operable across cloud-native environments. You will design and engineer runtime patterns for AI agents, MCP servers, APIs, and containerized services using AKS, Azure Container Apps, Kubernetes, queues, events, and cloud-native infrastructure; build reusable infrastructure patterns and platform APIs for consistent agent deployment and operation; define standards for agent manifests, runtime configurations, and tool definitions; standardize how agents connect to models, MCP servers, and enterprise APIs; implement approved patterns for networking, identity, and access control; develop paved-road templates, SDKs, reference implementations, documentation, and onboarding journeys; and ensure that all agent workloads are observable, scalable, secure, and production-ready with appropriate resilience, cost control, release promotion, and operational readiness practices. The role requires 7+ years of platform engineering experience with the specific cloud-native technology stack and the cross-functional collaboration skills to work effectively with AI Platform, AI Ops, Cybersecurity, Architecture, and Application teams.
Why This Agent Runtime Platform Engineer Role Is the AI Infrastructure Career of 2026 in Abu Dhabi: Senior platform engineers with 7+ years of cloud-native Kubernetes and Azure platform engineering experience — who combine deep hands-on AKS, Azure Container Apps, Terraform, and GitHub Actions expertise with working knowledge of AI agent architectures, MCP server patterns, and API/service contracts, plus strong networking, workload identity, API gateway, secrets management, and private connectivity skills, and the production readiness discipline covering observability, scalability, resilience, cost control, and release promotion — represent one of the rarest and most urgently needed engineering profiles in the enterprise AI infrastructure market globally in 2026. The opportunity to build Abu Dhabi’s enterprise AI agent runtime platform from first principles at this stage of the agentic AI adoption cycle is genuinely rare and career-defining.
Key Responsibilities
Agent Runtime & Platform Engineering
- Design and engineer runtime patterns for AI agents, MCP servers, APIs, and containerized services — applying deep Kubernetes and Azure cloud-native platform engineering expertise to define the specific runtime architecture patterns that enable AI agents and their supporting services (MCP tool servers, API integrations, queue consumers, event processors) to be deployed, operated, and scaled consistently across the enterprise, creating the runtime design standards that prevent every team from independently inventing incompatible agent deployment approaches that produce operational fragmentation and security gaps
- Build runtime capabilities using AKS, Azure Container Apps, Kubernetes, queues, events, and cloud-native infrastructure — implementing the specific Kubernetes workload configurations, Azure Container Apps environments, Azure Service Bus or Event Hub integrations, and supporting cloud-native infrastructure components that provide the agent runtime substrate, applying the production-grade configuration discipline (resource limits, health probes, disruption budgets, autoscaling policies, node selector and affinity rules) that makes agent workloads reliable at enterprise scale
- Build reusable infrastructure patterns and platform APIs for consistent deployment and operation of agent workloads — creating the Terraform modules, Helm charts, platform API specifications, and deployment templates that allow application teams to provision compliant, correctly configured agent runtime environments through self-service interfaces rather than requiring platform engineers to manually configure each new agent deployment, enabling the scale of agent deployment that enterprise AI adoption requires
- Define standards for agent manifests, runtime configurations, and tool definitions — establishing the specific YAML/JSON schema standards, configuration validation rules, and tool definition contracts that ensure all agent workloads deployed on the platform are described in a consistent, machine-readable format that supports automated deployment, configuration validation, and operational management at scale
Integration, Security, Networking & Identity
- Standardize how agents connect to models, MCP servers, and enterprise APIs — defining the specific network path, authentication mechanism, authorization policy, and traffic management approach that agents use to connect to LLM model endpoints (Azure OpenAI, deployed open-source models), MCP tool servers, and enterprise backend APIs, ensuring that all agent-to-service connectivity follows approved security patterns rather than ad-hoc implementation choices that create security gaps or operational inconsistency
- Implement approved patterns for networking, identity, and access control — applying Azure Workload Identity federation, Kubernetes Service Account token projection, Azure Private Endpoints, private DNS zones, Network Security Groups, ingress controller configurations, and API Gateway policies to implement the zero-trust networking and identity architecture that enterprise AI agent deployments require, preventing the agent connectivity security anti-patterns (service account over-privilege, public endpoint exposure, credential-based authentication) that create unacceptable security risk in production environments
- Develop paved-road templates, SDKs, reference implementations, documentation, and onboarding journeys for application teams — creating the complete self-service enablement package that allows application teams who want to deploy AI agents on the platform to do so correctly and confidently without requiring platform engineer involvement for each individual deployment, dramatically reducing the time-to-deploy for new agent workloads while maintaining the security, governance, and operational standards that enterprise deployment requires
- Manage Secrets Management and Private Connectivity — implementing Azure Key Vault CSI driver integration, external secrets operator patterns, and private connectivity architectures (VNet injection, Private Link, Private Endpoints) that ensure sensitive credentials, API keys, and connection strings used by agent workloads are managed through approved secrets management patterns rather than environment variable injection or container image embedding anti-patterns
Production Readiness — Observability, Scalability & Resilience
- Ensure agent workloads are observable, scalable, secure, and production-ready — implementing the structured logging, distributed tracing (OpenTelemetry), metrics collection (Prometheus/Azure Monitor), alerting, and dashboarding infrastructure that gives operations teams complete visibility into agent workload behavior, performance, and health in production, enabling proactive incident detection and rapid root cause analysis rather than reactive response to customer-reported failures
- Apply appropriate practices for resilience, cost control, release promotion, and operational readiness — designing the circuit breaker patterns, retry policies, graceful degradation mechanisms, horizontal pod autoscaling configurations, KEDA event-driven scaling rules, cost tagging and budget allocation approaches, GitOps-based release promotion pipelines (GitHub Actions → ArgoCD or equivalent), and operational readiness checklists that ensure agent workloads meet the production quality bar before they handle live enterprise traffic
- Collaborate with AI Platform, AI Ops, Cybersecurity, Architecture, and Application teams — maintaining the cross-functional engineering relationships and communication discipline that ensures the agent runtime platform meets the technical requirements, security standards, operational expectations, and developer experience needs of every stakeholder group that depends on the platform’s correctness, security, reliability, and usability for their AI application delivery
Full Required Technology Stack
Container Orchestration (Required): AKS (Azure Kubernetes Service) · Kubernetes (expert) · Azure Container Apps · Containers (Docker) — deep production experience required across all
Networking & Security (Required): Networking · Workload Identity · Ingress (NGINX/Traefik/AGIC) · API Gateways (Azure API Management) · Private Connectivity (Private Endpoints/VNet) · Secrets Management (Key Vault)
Infrastructure as Code (Required): Terraform — reusable module development · environment-aware provisioning · state management | GitHub Actions or equivalent CI/CD automation
AI Platform Knowledge (Required): Working knowledge of AI agent architectures · MCP server patterns · API/service contracts | Agentic application deployment and operation patterns
Production Engineering: Observability (OpenTelemetry · Prometheus · Azure Monitor) · Autoscaling (HPA · KEDA) · Resilience patterns · Cost control · Release promotion · GitOps
Experience: 7+ years of relevant platform engineering experience | Reusable platform module development · Deployment template design · Cross-team platform API development
Qualifications & Requirements
Required Profile
- 7+ years of relevant platform engineering experience — with demonstrated production delivery of cloud-native infrastructure at enterprise scale using Kubernetes, AKS, Azure Container Apps, and supporting Azure services
- Strong hands-on expertise across AKS, Azure Container Apps, Kubernetes, Containers, Networking, Workload Identity, Ingress, API Gateways, Private Connectivity, and Secrets Management — with production engineering experience across every item in this list, not theoretical familiarity
- Strong experience with Terraform and GitHub Actions or equivalent automation — for production-grade infrastructure as code and CI/CD pipeline automation
- Experience developing reusable platform modules, deployment templates, and environment-aware provisioning — for self-service platform delivery to application teams
- Working knowledge of AI agent architectures and API/service contracts — sufficient to design and implement the runtime patterns that AI agents and MCP servers require
About Enterprise AI Agent Runtime Engineering in Abu Dhabi 2026
The emergence of agentic AI — where autonomous AI agents use tools, call APIs, query data sources, and orchestrate multi-step workflows to complete complex tasks without step-by-step human direction — is creating a new and urgent class of enterprise infrastructure requirement that conventional cloud-native platform engineering has not previously needed to address. MCP servers, agent manifests, tool definitions, streaming agent responses, agent state management, and the specific networking and security patterns that AI-to-enterprise-API connectivity requires are genuinely new infrastructure concerns that demand platform engineers who combine deep Kubernetes and Azure cloud-native expertise with working understanding of how AI agents actually operate. Abu Dhabi’s extraordinary investment in AI platform capability — driven by government AI initiatives, enterprise digital transformation programs, and the region’s ambition to establish itself as a global AI infrastructure center — is creating significant demand for exactly this rare combination of cloud-native platform engineering depth and AI agent infrastructure understanding. The Agent Runtime Platform Engineer who joins this Abu Dhabi programme in 2026 will be building production infrastructure that defines how enterprise AI agents are deployed and operated across the UAE’s most ambitious AI transformation programmes — a career-defining opportunity at the genuinely earliest stage of the enterprise agentic AI infrastructure discipline.
Your Career Growth Path: Agent Runtime Platform Engineer → Staff Platform Engineer — AI Infrastructure → Principal Platform Architect — AI → Head of AI Platform Engineering → VP AI Infrastructure — a globally recognized, technically frontier, and commercially consequential platform engineering career trajectory built at the absolute leading edge of enterprise AI agent infrastructure in Abu Dhabi’s extraordinary AI ecosystem.
Who Should Apply?
- Senior Kubernetes Platform Engineers (7+ Years): With deep AKS, Azure Container Apps, Terraform, and cloud-native platform engineering production experience who want to specialize in enterprise AI agent runtime infrastructure in Abu Dhabi’s rapidly growing AI platform market
- Azure Cloud Native Engineers — AI Platform Transition: With comprehensive Azure cloud-native infrastructure backgrounds (AKS, networking, identity, API gateways, private connectivity) who are expanding into AI agent runtime infrastructure and want a dedicated AI platform engineering role in Abu Dhabi
- Platform Engineers — MCP & Agentic AI Interest: With strong Kubernetes and Terraform platform engineering foundations who have developed working knowledge of AI agent architectures, MCP server patterns, and agentic application deployment and want to build their career at the frontier of enterprise AI infrastructure
- DevOps/Platform Engineers — Enterprise Security Focus: With deep networking, Workload Identity, secrets management, private connectivity, and API gateway implementation experience who want to apply their security-first platform engineering expertise to the AI agent runtime infrastructure challenges that enterprise AI adoption in Abu Dhabi demands
- Cloud Infrastructure Engineers — UAE/GCC Based: Currently in the UAE or GCC with the required Kubernetes, AKS, Terraform, and Azure platform engineering experience who want to take on a senior agent runtime platform engineering role within Abu Dhabi’s most actively growing enterprise AI platform environment
Recently Opening Jobs👇
