Toshiba Global Commerce Solutions is seeking a Principal Software Engineer, Developer Experience Platform to build and operate the engineering system that moves work from requirement through local development, focused validation, CI/CD, deployment, and production feedback across a mature retail point-of-sale estate.
This is not an advisory architecture role. You will work directly in Java and JavaScript repositories, Maven builds, Docker and Kubernetes environments, GitLab/Jenkins pipelines, platform services, and OpenTelemetry traces. You will reproduce developer friction, write the critical code, and own improvements through rollout and adoption.
You will also direct agentic engineering workers, AI coding and analysis agents that execute bounded engineering tasks. You will supply context and constraints, decompose work, supervise execution, review and correct output, enforce deterministic gates, and remain accountable for every accepted change.
Developer Experience is treated as an engineering product, not a collection of tools. Success means engineers can onboard, build, test, provision, deploy, diagnose, and learn from production with less waiting, less cognitive load, and fewer manual handoffs.
Mission
Build a developer platform and agentic engineering operating model that lets engineers move from requirement to safe production feedback with less waiting, cognitive load, and manual coordination. Success is demonstrated through working software, faster feedback, reliable self-service, and sustained adoption, not through the volume of diagrams or standards produced.
Engineering Environment
• A mature POS estate spanning Java and JavaScript applications, Maven builds, Docker-based local and CI environments, Kubernetes and Helm deployment assets, and GitLab/Jenkins delivery workflows.
• Cloud and store-edge software with different connectivity, rollout, recovery, security, and operational constraints.
• A large business-facing Cucumber BDD suite with browser-driven validation, seeded test data, service dependencies, parallel execution, and release-gating implications.
• Existing OpenTelemetry-based diagnostics and an evolving browser-automation path that must be integrated into useful developer feedback rather than operated as isolated tooling.
• An expanding agentic engineering model in which AI workers assist with analysis, implementation, testing, review, triage, and release preparation under controlled access and deterministic validation.
Key Responsibilities
Hands-On Platform Engineering and Technical Ownership
• Personally build and operate developer-facing services, command-line tools, integrations, templates, reusable pipeline libraries, portal or catalog capabilities, and environment automation.
• Use profiling, logs, metrics, traces, and controlled experiments to reproduce bottlenecks and eliminate root causes across local, CI, environment, deployment, and production-feedback workflows.
• Carry critical changes from design through code, migration, deployment, runbooks, adoption, and operational follow-through. A recommendation is not complete until the path works for engineers.
Developer Experience as an Engineering Product
• Map the end-to-end developer journey, identify the highest-cost friction, maintain a prioritized improvement roadmap, and publish clear ownership and service expectations.
• Use developer research, workflow telemetry, support demand, and delivery data to distinguish systemic constraints from isolated preferences.
• Maintain platform contracts, lifecycle policies, deprecation paths, and architecture decisions that are backed by working reference implementations and practical migration plans.
Onboarding, Local Development, and the Inner Loop
• Create reproducible, automated setup for source access, dependencies, configuration, secrets, local services, test data, and development environments.
• Improve edit-build-test-debug cycles through caching, incremental execution, targeted validation, realistic mocks or service virtualization, and actionable local diagnostics.
• Replace tribal knowledge with executable documentation, ownership metadata, runbooks, and self-service guidance that remains current because it is part of the delivery system.
Golden Paths and Platform Self-Service
• Define paved roads for services, APIs, user interfaces, data and configuration, security, observability, testing, deployment, and store-edge components.
• Build and govern templates, scaffolding, reusable libraries, pipeline components, policy automation, and a developer portal or catalog that exposes ownership, documentation, dependencies, health, and support paths.
• Enable on-demand and ephemeral environments, preview environments, seeded or synthetic test data, automated access, and lifecycle cleanup through infrastructure as code.
CI/CD, Release, and Production Feedback
• Engineer and evolve developer-facing GitLab/Jenkins workflows that provide fast, tiered feedback for local changes, merge requests, integration, release qualification, deployment, and post-deployment verification.
• Improve build and pipeline performance through profiling, caching, parallelism, dependency discipline, failure isolation, and clear separation of product, test, environment, and infrastructure failures.
• Standardize artifact versioning, provenance, promotion, rollback, SBOM generation, signing, and software-supply-chain controls without turning routine delivery into a ticket queue.
• Put logs, metrics, traces, deployment state, runbooks, and production signals directly in the engineer's workflow so failures can be understood and acted on quickly.
Agentic Engineering Workforce
• Build controlled integrations that give agentic workers least-privilege, auditable access to source code, work items, documentation, build systems, test systems, environments, and telemetry.
• Turn ambiguous engineering goals into bounded agent work packets with repository context, architecture constraints, acceptance criteria, risk, test strategy, stop conditions, and escalation paths.
• Supervise concurrent agent work, detect stalls, loops, scope drift, and conflicting solutions, and intervene directly when the work requires Principal-level judgment.
• Review, test, and modify agent-produced code and analysis. Require deterministic build, test, security, policy, and observability evidence before accepting output.
• Create reusable agent skills and workflows for analysis, implementation, refactoring, test authoring, pipeline changes, documentation, defect triage, and release preparation.
• Measure accepted output, rework, defect impact, cycle-time change, and execution efficiency so agentic capacity compounds team output without reducing quality or accountability.
Measurement and Continuous Improvement
• Own DORA measures together with developer-flow indicators such as onboarding time, feedback speed, build and test duration, environment wait, merge-request cycle time, cognitive load, satisfaction, and tooling or process friction.
• Create end-to-end observability for the developer platform using OpenTelemetry or equivalent instrumentation, including queueing, environment readiness, pipeline stages, failure classes, and deployment latency.
• Use baselines, service-level objectives, experiments, and adoption data to demonstrate measurable improvement without degrading quality, reliability, security, or developer sustainability.
Required Qualifications
• Substantial software engineering or platform engineering experience, including sustained Staff or Principal-level influence across multiple teams or products.
• A recent hands-on track record of personally shipping and operating production platform code, automation, integrations, or delivery infrastructure.
• Demonstrated success designing and delivering internal developer platforms, developer-productivity capabilities, or large-scale engineering enablement used in complex production env
