Senior Multi-Cloud Platform Engineer

Job description

JLL empowers you to shape a brighter way.   Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong.  Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward.    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 JLL's 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 JLL's 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&#43; 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 • <p