Bridgestone MobilitySolutions

Lead GenAI / LLM Engineer –

- AI-Enabled Software Development

About Bridgestone MobilitySolutions (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 throughexperimentation.

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 Lead GenAI / LLM Engineer focused on AI-enabled software development. This role will provide technical leadership for GenAI / LLM Engineers within the AI-SDLC Enablement team while educating teams, coaching adoption, configuring tools, and scaling approved AI developer assistants such as GitHub Copilot, Claude Code, and similar platforms across software development teams.

The role will partner with product owners, software engineers, QA/QE, DevOps, architecture, security, governance, and team champions to lead reusable AI-SDLC patterns, guide other engineers, improve developer productivity, and ensure adoption is measurable, secure, and aligned with enterprise standards.

Responsibilities

AI-SDLC Leadership &

- Developer Productivity

- Lead the AI-SDLC Enablement engineering workstream, setting technical direction, delivery priorities, reusable standards, and adoption approach for AI-enabled software development.
- 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.
- Provide day-to-day technical guidance, coaching, and review for GenAI / LLM Engineers building enablement assets, pilots, patterns, and adoption materials.
- 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.
- Own adoption and outcome measurement across pilots and scaled rollouts, including time saved, code quality, test coverage, defect reduction, cycle time improvement, and developer experience.





AI-Enabled Engineering Patterns &

- Integration

- Partners with teams 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.
- Establish engineering review practices for reusable prompts, custom instructions, context repositories, agent skills, workflow examples, and AI-SDLC bootstrap assets.

Governed Rollout, Training &

- Continuous Improvement

- Lead onboarding, champion training, demos, office hours, and feedback loops for AI-SDLC adoption while ensuring other GenAI / LLM Engineers can independently support team enablement.
- 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.
- Coordinate work across GenAI / LLM Engineers, team champions, product owners, and platform stakeholders to ensure milestones, dependencies, handoffs, and acceptance criteria are clearly managed.
- Ensure AI-SDLC practices comply with enterprise security, architecture, data privacy, source code, software quality, and AI governance standards.

Experience

- 8+ years of experience in software engineering, DevOps, software delivery, developer enablement, AI engineering, or related technology roles.
- 3+ years of hands-on experience using, deploying, or enabling GenAI/LLM tools for software development, testing, documentation, automation, or developer productivity.
- Demonstrated experience leading engineers or technical teams through new tools, engineering practices, automation patterns, software delivery improvement, or AI-enabled transformation.
- Experience mentoring GenAI / LLM Engineers, reviewing technical deliverables, coordinating work across multiple teams, and driving adoption outcomes from pilot to scale.

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.




- Ability to set standards for reusable AI-SDLC assets, review contributions from other engineers, and distinguish enterprise-reusable patterns from team-specific extensions.
- 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

- Strong 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.
- Ability to guide technical design reviews, code quality practices, AI-generated code validation, testing approaches, and engineering workflow improvements across teams.

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.
- Experience leading adoption across multiple teams, managing enablement backlogs, sequencing rollout activities, and reporting progress, risks, and measurable outcomes to stakeholders.

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 recent engineering tools.
- Ability to translate enterprise governance expectations into practical engineering guardrails, review practices, and reusable guidance for GenAI / LLM Engineers and development teams.
- 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 leading AI-assisted software development enablement 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, coaching, and engineering standards that teams and GenAI / LLM Engineers can apply consistently.

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