Principal Platform Engineer || Agentic AI || Internal Developer Platform

About the role

The role delivers production Internal Developer Platform capabilities as a hands-on technical resource. You will architect and operate the platform that allows product teams to build and release software with minimal friction. Collaboration with Platform Engineering and adjacent teams supports standards and the IDP roadmap. You will own the implementation of service catalogue entries, self-service workflows, scaffolders, and golden path templates. This position requires designing CI/CD integration patterns that streamline deployments and operations for consumer teams. You will create and maintain developer-facing documentation, guides, and enablement materials. Platform reliability and performance issues will be identified and resolved by you to reduce development team friction.

Key facts

What you'll do

Product teams consume reliable platform foundations through end-to-end implementation, configuration, and maintenance of the Internal Developer Platform.

You will design, build, and operate service catalogue entries that provide standardized capabilities for platform users.

CI/CD integration patterns are built and maintained so product teams can consume streamlined deployments and operations.

Developer-facing documentation, guides, and enablement materials are created and maintained to support correct and efficient platform usage.

You will investigate and resolve platform reliability and performance issues to reduce development team friction.

Close collaboration with the Platform Lead and platform teams such as Base Tech, Auth, and Nexus achieves shared goals and clear interfaces.

Production AI applications are designed, built, and shipped by you with a focus on real-world operational reliability.

You will implement and operate service catalogues, scaffolders, golden paths, and self-service infrastructure workflows.

Familiarity with integrating IDP tooling into CI/CD pipelines and cloud-native infrastructure is demonstrated through hands-on implementation.

Strong engineering capability across the NGA stack is exercised daily through work in Backend Go, Frontend React with Next.js, Messaging/Streaming Apache Kafka or RedPanda, and Data PostgreSQL.

Infrastructure as Code and DevOps toolchains are operated effectively as part of standard practice to ensure reproducibility.

Understanding of event-driven and event-based architecture patterns is applied for correct platform integration and scalability.

Comfortable operation in cloud-native environments such as Kubernetes on AKS, containers, and GitOps is required to manage platform lifecycle.

Strong understanding of developer experience principles and platform thinking guides platform decisions and trade-offs.

Supporting and enabling development teams in a platform capacity requires operating across platform and product team boundaries to ensure cohesion.

Requirements

Demonstrable, hands-on experience designing, building, and shipping production AI applications is required to validate architectural decisions in live environments.

Hands-on experience with Internal Developer Platforms in production is mandatory, using tools such as Backstage, Port, Cortex, or comparable platforms.

Service catalogues, scaffolders, golden paths, and self-service infrastructure workflows must be implemented and operated by the candidate to enable self-service adoption.

Familiarity with and practical engagement in integrating IDP tooling into CI/CD pipelines and cloud-native infrastructure is required for seamless developer workflows.

Strong engineering capability across the NGA stack is required, including Backend Go, Frontend React with Next.js, Messaging/Streaming Apache Kafka or RedPanda, and Data PostgreSQL, or rapid proficiency toward this level.

Infrastructure as Code and DevOps toolchains must be operated effectively as part of standard practice to manage infrastructure lifecycle.

Understanding of event-driven and event-based architecture patterns is necessary for correct platform integration and system resilience.

Comfortable operation in cloud-native environments such as Kubernetes on AKS, containers, and GitOps is required to ensure platform reliability and scalability.

Practical notes

Employment at IFS follows local legal requirements, and team details are confirmed during the process. Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Internal Developer Platforms enable development teams to self-serve infrastructure through automation and standardized templates.

Platform engineering reduces friction for software delivery by abstracting complex infrastructure into consumable services for consumers.

Modern IDPs commonly integrate with tools such as Backstage, Port, Cortex, Kubernetes, and GitOps pipelines in enterprise environments.

Building intelligent products for enterprise scale often combines cloud-native infrastructure, event-driven messaging, and CI/CD automation.

Production AI system design demands experience deploying and operating intelligent applications in real operational environments with reliability and performance considerations.