IT Systems Engineering, Platform Engineer

Marsh McLennan

Platform Engineer working with DevOps teams to implement innovative technologies at Marsh Tech. Involves automating deployments and supporting Kubernetes and container-based applications.

Posted 6/30/2026full-timePune • 🇮🇳 IndiaMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKubernetesVault

About the role

Key responsibilities & impact

Automate, write code, and build pipelines, working hand in hand with Software Engineering teams on automating deployments and integrations to PaaS services.
Leverage AI capabilities as applicable.
Act as a consultant and subject matter expert for Senior MMC Leadership, Project Managers, Software and Engineering teams, to navigate the constantly changing technology landscape and identify appropriate tactical and strategic solutions to deliver self-service, automated cloud, and container-based application environments.
Deploy and support Kubernetes and related technology stacks.
Engineer and implement new technology solutions, on premise and in public cloud.
Obtain and maintain an in-depth understanding of cloud native technology to ensure all appropriate options are considered to support business initiatives.
A service delivery mindset with a requirement to participate in on-call rota to provide 24*7 support.

Requirements

What you’ll need

Design, write and maintain YAML definitions for deploying Kubernetes apps with ArgoCD
Design, write and maintain YAML manifests for deploying infrastructure with Crossplane
Subject matter expert for Kubernetes as well as one or more of the following technologies: JFrog Artifactory, HashiCorp Vault, Komodor, Tigera Calico Enterprise, AWS EKS, Azure AKS, CASTAI
Experience with AWS/Azure/GCP/OCI public cloud; development and architecture.
Experience with REST APIs to support integration of infrastructure, cloud, and platform as a service technology.
Familiarity with using AI tools to support: scripting and automation, troubleshooting, documentation generation, test case creation, code review assistance
Basic understanding of LLMs / generative AI concepts prompts hallucinations / accuracy, limitations security considerations, responsible use of AI in engineering workflows
Ability to evaluate AI-generated outputs for accuracy, security, and maintainability
Exposure to automation/platform tooling where AI may be used, such as: CI/CD pipelines, observability / monitoring, incident response, workflows, infrastructure scripting
Development experience with web-based applications and APIs (RESTful), web services-based applications, data platforms/services.
API Management platforms.
Proven ability to deliver customer focused shared service platforms
Proven ability to work from inception to production building and delivering software
A commitment to best practices and striving for the elimination of manual operational processes to promote a DevOps oriented software delivery model.

Benefits

Comp & perks

Professional development opportunities
Flexible working arrangements

ATS Keywords

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Hard Skills & Tools

KubernetesYAMLREST APIsCloud Native TechnologyAutomation ScriptingCI/CD PipelinesWeb Services DevelopmentInfrastructure as CodeContainerizationMonitoring and Observability

Soft Skills

Customer FocusConsultative ApproachProblem SolvingCollaborationAdaptability