Bridgestone Mobility Solutions

GenAI / LLM Engineer –

- AI-Enabled Software Development

About Bridgestone Mobility Solutions (BMS)

We are Bridgestone Mobility Solutions, the digital product factory for the largest global tire company, and we are a team committed to product innovation in transportation and mobility space.

With work spanning everything from tire integrated sensor and roadway vision recognition R&D; to building mobile service and commercial vehicle marketplaces, we are defining the interconnected future of digital vehicles.

We are a team with a strong commitment to customer-driven innovation, data-based decision-making, and a commitment to learning through experimentation.

As a part of the Bridgestone team, the opportunities are endless across a broad spectrum of businesses in the Bridgestone portfolio.

Our culture of learning and growth has enabled our engineers to expand their skills across the full product value chain—from understanding customer needs to designing, building, and continuously improving digital products that create measurable business value.

Job brief

Bridgestone GCC is building an AI-SDLC enablement capability to help software development teams plan, design, build, test, deploy, and operate solutions faster using approved AI-enabled development tools, reusable patterns, and responsible AI guardrails.

We are looking for a hands-on GenAI / LLM Engineer focused on AI-enabled software development. This role will work within the AI-SDLC Enablement team to educate, coach, configure, and deploy tools such as GitHub Copilot, Claude Code, and other approved AI developer assistants across software development teams.

The role will be partnered with product owners, software engineers, QA/QE, DevOps, architecture, security, governance, and team champions to improve developer productivity, documentation quality, code quality, test coverage, delivery predictability, and speed to market.

Responsibilities

AI-SDLC Enablement &

- Developer Productivity

- Enable development teams to apply AI-assisted practices across requirements, design, coding, testing, documentation, deployment, and support.
- Configure, pilot, and scale approved AI developer tools such as GitHub Copilot, Claude Code, and other enterprise-approved coding assistants in development team workflows.
- Develop reusable prompts, skills, templates, context repository patterns, and spec-driven development practices that help teams consistently use AI across the SDLC.
- Coach developers, product owners, business analysts, QA/QE engineers, and team champions on secure and effective AI-assisted software development practices.
- Measure adoption and outcomes,



including time saved, code quality, test coverage, defect reduction, cycle time improvement, and developer experience.

AI-Enabled Engineering Patterns &

- Integration

- Partner with teams to apply AI to user stories, technical design, code explanation, refactoring, unit tests, test plans, documentation, pull requests, and defect analysis.
- Create and maintain reusable enablement assets, including onboarding guides, training materials, playbooks, prompt libraries, workflow examples, and handoff documentation.
- Align AI-enabled development practices with Azure DevOps, GitHub, CI/CD pipelines, code repositories, testing tools, documentation repositories, and approved enterprise delivery standards.

Governed Rollout, Training &

- Continuous Improvement

- Lead onboarding, champion training, demos, office hours, and feedback loops for AI-SDLC adoption.
- Define guardrails for AI-assisted development, including code review expectations, human-in-the-loop validation, secure prompt practices, intellectual property protection, and responsible AI usage.
- Capture pilot feedback, document lessons learned, and scale proven AI-enabled development patterns to additional teams.
- Ensure AI-SDLC practices comply with enterprise security, architecture, data privacy, source code, software quality, and AI governance standards.

Experience

- 5+ years of experience in software engineering, DevOps, software delivery, developer enablement, AI engineering, or related technology roles.
- 2+ years of hands-on experience using or enabling GenAI/LLM tools for software development, testing, documentation, automation, or developer productivity.
- Demonstrated experience educating, coaching, or enabling technical teams through new tools, engineering practices, automation patterns, or delivery transformation.

Technical Requirements AI-Enabled Software Development Tools

- Hands-on experience with AI developer tools such as GitHub Copilot, Claude Code, Cursor, Amazon Q Developer, Microsoft Copilot, or similar coding assistants.
- Strong understanding of how LLMs can support requirements analysis, technical design, code generation, code review, refactoring, test generation, documentation, and troubleshooting.
- Ability to design reusable prompts, custom instructions,



team playbooks, context repositories, and workflow patterns for AI-assisted development.
- Knowledge of responsible AI for software engineering, including secure coding, IP protection, code provenance, data privacy, hallucination risk, and human validation of AI-generated outputs.

Software Engineering &

- DevOps

- Solid software engineering foundation with practical experience in one or more modern languages such as Java, JavaScript/TypeScript, Python, C#, or related enterprise development stacks.
- Experience with SDLC practices, Agile delivery, backlog refinement, user stories, technical documentation, code review, unit testing, quality engineering, and release readiness.
- Familiarity with GitHub, Azure DevOps, Git workflows, pull requests, CI/CD pipelines, automated testing, and developer workflow automation.

Enablement, Adoption &

- Measurement

- Experience creating technical training, adoption playbooks, demos, office hours, job aids, and reusable developer guidance.
- Ability to work directly with development teams to observe workflows, identify friction, introduce AI-assisted practices, and measure before-and-after productivity outcomes.
- Comfortable facilitating technical workshops with developers, QA/QE, product owners, business analysts, DevOps engineers, architects, and security stakeholders.
- Experience defining adoption metrics, productivity measures, quality indicators, and continuous improvement loops.

Governance, Security &

- Enterprise Fit

- Understanding of enterprise software delivery controls, secure development practices, source code protection, data classification, access management, and approved tool governance.
- Experience partnering with architecture, security, compliance, platform, and governance teams to define safe adoption practices for new engineering tools.
- Familiarity with cloud-native development, APIs, containerized applications, observability, and production support practices is preferred.

Preferred Qualifications

- Bachelor’s or master’s degree in Computer Science, Computer Engineering, Data Science, Applied Mathematics, or a related technical discipline.
- Experience enabling AI-assisted software development using GitHub Copilot, Claude Code, or similar tools across multiple teams or products.
- Familiarity with AI governance, secure software delivery, source code protection, model output validation, IP risk, privacy, and responsible AI practices.
- Ability to translate AI-enabled development concepts into practical guidance, examples, playbooks, and coaching that development teams can immediately apply.

📌 Gen AI LLM Engineer (Karnataka)
🏢 Bridgestone Americas
📍 Karnataka