Senior Multi-Cloud Platform Engineer

About The Role

We are seeking an experienced Senior Multi-Cloud Platform Engineer to design, implement, and maintain our cloud infrastructure with a primary focus on AWS. You will be a cornerstone of JLLs Platform Engineering team, architecting secure, scalable, and highly available cloud solutions that underpin enterprise technology services across the organization.

This is a hands-on engineering role that also carries meaningful technical leadership responsibilities. You will mentor junior engineers, lead platform initiatives, and collaborate with cross-functional stakeholders — translating complex infrastructure requirements into reliable delivery outcomes. Experience with AI tooling, including Model Context Protocol (MCP) integrations, is essential as we continue to evolve our platforms to support intelligent, AI-augmented workflows.

Key Responsibilities

Cloud Architecture & Infrastructure

• Design, implement, and optimize AWS-based infrastructure as the primary cloud environment, including compute, storage, networking, identity, and security services
• Implement scalable, fault-tolerant platform solutions that meet enterprise performance, reliability, and compliance requirements
• Lead cloud modernization and infrastructure transformation initiatives aligned with JLLs technology roadmap
  
  

Platform & DevOps Engineering

• Develop and maintain infrastructure as code (IaC) using Terraform, AWS CloudFormation, and Azure ARM/Bicep templates
• Build, manage, and optimize containerized workloads using EKS (primary) and AKS, leveraging Karpenter for dynamic node provisioning
• Deploy and operate service mesh solutions (Istio) and GitOps workflows (ArgoCD) across container environments
• Design, build, and maintain CI/CD pipelines using GitHub Actions for automated testing, security scanning, and deployment workflows
  
  

AI Platform & MCP Integration

• Implement and maintain Model Context Protocol (MCP) server integrations, enabling AI agents and LLM-powered applications to interact securely with enterprise systems and data sources
• Evaluate and adopt emerging AI infrastructure patterns (RAG pipelines, agentic frameworks, AI gateway layers) into the platform engineering practice
• Ensure AI/ML workloads meet security, observability, and scalability standards consistent with enterprise platform requirements
  
  

Security, Governance & Cost Management

• Establish, enforce, and continuously improve cloud security standards, identity/access management policies, and compliance frameworks across environments
• Implement infrastructure-level controls supporting regulatory and governance requirements
• Drive cloud cost optimization through resource right-sizing, reserved capacity strategies, and FinOps practices
• Build observability and monitoring solutions using Datadog or equivalent platforms to ensure platform health and SLA compliance
  
  

Technical Leadership & Collaboration

• Provide technical guidance and mentorship to junior and mid-level platform engineers, fostering growth in systems development and cloud engineering practices
• Lead systems development projects and infrastructure initiatives, coordinating delivery across engineering teams and business stakeholders
• Communicate infrastructure designs, platform recommendations, and technical trade-offs clearly to stakeholders across engineering, architecture, and business units
• Troubleshoot complex, high-impact issues across cloud environments and drive root cause resolution
  
  

Required Qualifications

• 5+ years of hands-on experience in cloud platform or infrastructure engineering roles
• Deep, production-grade expertise with AWS (primary): EC2, EKS, RDS, S3, VPC, IAM, Lambda, CloudWatch, and related services
• Demonstrated experience with container orchestration using Kubernetes; strong proficiency with EKS required, AKS experience valued
• Hands-on experience with Karpenter, ArgoCD, and Istio in production Kubernetes environments
• Advanced proficiency with infrastructure as code tools, particularly Terraform; experience with AWS CloudFormation and/or Azure ARM/Bicep
• Experience integrating or operationalizing AI/ML workloads on cloud platforms, including familiarity with agentic frameworks, LLM APIs, vector stores, or model serving infrastructure
• Working knowledge of MCP (Model Context Protocol) — ability to deploy, configure, and maintain MCP servers that connect AI agents to enterprise tools and data sources
• Demonstrated use of AI-assisted development practices — leveraging AI tooling, LLM models, and agentic workflows to accelerate engineering, automate repetitive tasks, and improve delivery quality and productivity
• Expertise in cloud security principles: network segmentation, IAM least-privilege, secrets management, vulnerability scanning, and compliance automation
• Advanced proficiency with GitHub and GitHub Actions for CI/CD workflow design and automation
• Advanced knowledge of cloud networking: VPCs, peering, transit gateways, DNS, load balancing, and private connectivity patterns
• Strong communication skills with the ability to convey complex technical concepts to both engineering peers and non-technical stakeholders
• Self-directed work ethic with demonstrated ability to manage complex, long-horizon projects independently
  
  

Preferred Qualifications

• AWS certifications: Solutions Architect (Associate or Professional), DevOps Engineer, or Security Specialty
• Azure certifications: Solutions Architect Expert, DevOps Engineer Expert, or Security Engineer Associate
• Hands-on experience with Azure and/or GCP in production or enterprise environments
• Experience building or maintaining AI agent frameworks (e.g., Bedrock, AgentCore, Azure Foundry) on cloud infrastructure
• Familiarity with enterprise observability platforms, particularly Datadog (APM, infrastructure monitoring, log management)
• Background in supporting enterprise-scale, multi-tenant applications with high availability and strict SLA requirements
• Experience with hybrid cloud architectures and on-premises-to-cloud integration patterns
• Knowledge of FinOps practices and cloud cost governance tooling (AWS Cost Explorer, CloudHealth, or similar)
  
  

We are seeking a proactive cloud professional with deep AWS expertise and strong multi‑cloud experience, able to work independently on complex challenges and deliver secure, scalable, and resilient cloud platforms. The role applies established systems development methodologies, infrastructure and software architecture principles, and modern platform engineering practices to enhance JLL’s enterprise cloud capabilities.

The position incorporates the applied use of AI within cloud platforms, including leveraging AI services and agent‑based frameworks to improve automation, operational efficiency, and platform reliability. This includes supporting AI/ML workloads, integrating intelligent agents into cloud workflows where appropriate, and ensuring AI usage is aligned with enterprise standards for security, governance, observability, and cost management.

The role carries technical leadership expectations, including mentoring junior engineers, influencing engineering standards, and leading platform initiatives across cross‑functional teams to deliver dependable, enterprise‑grade cloud solutions.

JLL is committed to hiring and developing the most talented people. Equal Opportunity Employer.

---