AI-Native Product Engineer

Toshiba Global Commerce Solutions - External • Durham, NC

19h • $140,000 - $165,000 • Onsite

About The Position

Toshiba Global Commerce Solutions is seeking a Principal AI-Native Product Engineer. This is a builder seat with a senior architect's responsibility. You write code, ship code, and own the result, as well as the foundation everything else is built on. You will run multiple agentic work streams in parallel, with requirements clarified in code, UX built in code, tests generated in code, and documentation emitted from code. The entire lifecycle compresses around real, executing software on a scalable architecture. You are handed a problem and build the answer, which includes the working architecture, UX, code, tests, telemetry, and release, all shipping together because they were built together. This role offers a job where strong architecture and fast delivery are not in tension.

Requirements

• 8+ years of professional software engineering experience.
• Deep fluency with SOLID principles, clean architecture, separation of concerns, and design for testability.
• Ability to defend architectural calls under pressure.
• Track record of building systems that other engineers, and AI agents, can safely extend in parallel.
• Strong full-stack delivery background across frontend, backend, APIs, data, and integration.
• Experience with contract-first design using OpenAPI, AsyncAPI, JSON Schema, or typed schema systems.
• Hands-on experience with AI-assisted or agentic development tools such as Claude Code, Cursor, Codex, or Copilot.
• Comfort running multiple concurrent sessions in worktrees, or ability to ramp into that pattern quickly.
• Comfort building user-facing experiences directly in code, including layout, interaction states, accessibility, validation, and error handling.
• Strong test mindset; comfortable producing unit, integration, contract, UI, and regression tests as part of delivery.
• Experience with CI/CD, PR review, static analysis, automated quality gates, and release readiness.
• Ability to evaluate AI-generated code for correctness, maintainability, security, and architectural fit.
• Willingness to reject code that compromises the foundation.
• Strong context-switching discipline; ability to direct multiple parallel work streams without losing focus.
• Excellent written communication evident in code, PR descriptions, architectural notes, and release notes.
• Ownership mindset; accountability for what runs in production with your name on it.

Nice To Haves

• Demonstrated experience designing architectures specifically to scale with AI generation: clear boundaries, explicit contracts, encoded standards, agent-friendly seams.
• Built or contributed to reusable AI skills, prompt flows, MCP tools, RAG systems, or agent orchestration platforms.
• Experience authoring agentic developer tooling that other engineers actually adopted.
• Experience running concurrent multi-session AI workflows in production, with measurable velocity gains.
• Principal, staff, or senior-level engineering experience in enterprise software.
• Experience with retail, payments, POS, loyalty, store systems, SaaS, or edge computing platforms.
• Experience with microservices, event-driven systems, OpenAPI and AsyncAPI, cloud-native platforms, and observability.
• Experience implementing accessible, production-grade UX directly in modern frontend frameworks.
• Experience with DORA, SPACE, SLOs, error budgets, and production reliability.
• Security-aware engineering experience, including SAST, dependency scanning, secrets scanning, RBAC, and threat modeling.

Responsibilities

• Hold the architectural line, ensuring SOLID principles are non-negotiable and clear module boundaries with explicit contracts are maintained.
• Prioritize composability over cleverness, using small, single-purpose components with predictable inputs and outputs.
• Employ contract-first design using OpenAPI, AsyncAPI, typed schemas, and shared data contracts before implementation.
• Utilize tests as architectural guardrails, ensuring every contract and public interface has a test.
• Build observability in from the start, including structured logs, metrics, and traces.
• Design for change, ensuring the architecture survives rapid feature development, refactoring, and replacement.
• Reject AI-generated code that compromises the foundation, even if it appears to work.
• Own the feature from intent to production, ensuring speed and quality.
• Clarify stakeholder problems in code and conversation.
• Establish architecture and contracts before implementation runs at scale.
• Implement backend, frontend, APIs, data, and integration changes with agentic acceleration on clean abstractions.
• Build UX in code, in flow, validated against the running build.
• Generate tests alongside implementation, verified deterministically.
• Design in security, performance, observability, and release readiness, not retrofitting.
• Watch telemetry after release, fix issues, and feed learnings back into contracts, skills, and architecture.
• Write real code in real branches against real production systems.
• Merge PRs, not slide decks.
• Deploy, watch telemetry, and fix issues identified by telemetry.
• Build UX directly in code, including layout, interaction states, accessibility, validation, and error handling, with AI assistance.
• Codify what worked and roll repeated UX patterns back into shared components, prompts, and skills.
• Onboard into the agentic environment, ramping on the platform and reaching concurrent multi-stream operation early.
• Get hands-on with the internal AI developer portal, agent registry, prompt flow library, RAG service, and skills catalog.
• Pair with senior engineers on real agentic delivery and ship within the first two weeks.
• Configure local agentic stack: Claude Code with worktrees, MCP servers, tool integrations, test agents, review agents, and telemetry hooks.
• Move from single-session to concurrent multi-stream operation by the end of the first month.
• Author a reusable skill or prompt flow within the first 30 days.
• Ship at least one customer-visible feature end-to-end within the first 60 days.
• Orchestrate agentic swarms for daily work, directing them with context and rejecting anything that fails the architectural bar.
• Author new skills and prompt flows when a pattern is worth repeating.
• Invest in verification skills to teach agents to check their own work.
• Refactor and improve existing skills and contribute MCP tools.
• Treat the skills catalog as a first-class codebase.
• Encode architectural standards into skills and prompts.
• Generate API docs from contracts and verify against running services.
• Generate architecture notes alongside implementation.
• Produce runbooks and release notes from the actual change set.
• Produce onboarding docs for features as part of shipping them.
• Use AI to generate tests (unit, integration, contract, UI, regression) alongside implementation.
• Run real test suites in real pipelines.
• Use AI to identify edge cases, regression risk, and performance hot spots, then verify with deterministic checks.
• Prepare release readiness evidence as a build artifact.
• Watch features in production after release and fix issues if telemetry indicates problems.

Benefits

• Eligible for up to 8% performance-based bonus (individual and company performance)
• Group health coverage (medical, dental, & vision)
• Employee Assistance Programs
• Pre-tax spending accounts
• 401(k) plan (with company match)
• Company provided life insurance
• Pet Insurance
• Employee discounts
• Generous paid holiday schedule, paid vacation & sick/personal days

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed