Toshiba Global Commerce Solutions is seeking a hands-on Lead Software Engineer to drive end-to-end solution delivery for major retail platforms, and to make AI-augmented engineering the way our teams build.

In this role, you will own hands-on implementation (feature development, technical design, code reviews), engagement leadership (client-facing technical lead, trade-off decisions, mentoring), and operational ownership (delivery sign-off, post-release support) of high-impact solutions, translating business requirements into working software you help build. You will also be the engineering authority on how AI is used across the delivery lifecycle: designing agentic workflows, setting guardrails, measuring impact, and bringing the team along. You will drive Dual-Track Agile processes, using AI to compress the Discovery Track (rapid prototyping and feasibility validation) and to raise quality, traceability, and measurable outcomes in the Delivery Track.

The ideal candidate is a senior software engineer who codes daily and treats AI agents as a core part of their toolchain. You have moved beyond personal use of AI assistants to building repeatable, governed, measurable AI workflows that other engineers adopt, from analysis and design to testing, documentation, and code generation, while bringing the technical leadership to drive client engagements forward. You will mentor teams, make critical technical trade-offs, and take full accountability for delivery sign-off and post-release support.

This role requires strong retail platform domain knowledge (POS systems, store operations), cloud-native development experience, and deep fluency with generative and agentic AI tools to accelerate delivery while preserving code quality and architectural integrity. You will have the judgment to know when AI output can be trusted and when it cannot, and you will be a key driver of engineering excellence, ensuring solutions are both innovative and operationally robust.

Key Responsibilities

Design and operate AI/agentic workflows across the software lifecycle, including requirements analysis, design, prototyping, code generation, test-case creation, specification drafting, code review, documentation, and automated validation, and use them in your own daily development.
Define team standards for AI-assisted development: prompt and context practices, reusable agent skills and instructions, repository conventions, and review expectations for AI-generated code.
Establish guardrails for responsible AI use, including IP and licensing, secrets and data handling, security review of generated code, and client contractual constraints, and ensure ethical compliance across the team.
Build evaluation and validation around AI output (automated tests, specification conformance, static analysis, human-in-the-loop checkpoints) so that speed gains do not erode quality.
Define and report metrics on AI impact, such as cycle time, review turnaround, defect escape rate, test coverage, rework, and cost per outcome, and use them to decide where AI helps and where it does not.
Evaluate, pilot, and recommend AI tools, models, and integrations (IDE assistants, CLI agents, MCP servers, internal automation), considering cost, security, and vendor risk.
Build internal tooling and automation on LLM APIs where it removes recurring toil, such as PR summarization, test generation, release notes, and defect triage.
Own end-to-end solution delivery across Discovery and Delivery Tracks, personally building validated prototypes, contributing to formal designs, decomposing work into deliverables, and shipping accepted technical implementations.
Drive the Discovery Track under Dual-Track Agile by writing AI-accelerated working prototypes and demoing solutions to validate feasibility and align stakeholders before full implementation.
Translate discovery outcomes into delivery backlog items and partner with implementation teams to ensure design intent, code quality standards, and non-functional requirements are preserved throughout delivery.