Requirements
- 3+ years of experience in software engineering, developer tooling, technical enablement or a similar technical role.
- Practical experience using AI assistants for software development, such as Claude Code, GitHub Copilot, Cursor or similar tools.
- Strong understanding of AI-assisted software development workflows and the software development lifecycle.
- Experience defining and implementing best practices, patterns, standards and guardrails for AI usage within development teams.
- Hands-on experience with Claude AI / Anthropic tools is highly desirable.
- Understanding of prompt engineering and effective interaction with LLM-based development assistants.
- Experience evaluating AI-generated code and establishing practices for code quality, security, testing and maintainability.
- Ability to identify suitable development activities for AI-assisted automation and augmentation.
- Experience delivering technical training, workshops, mentoring or coaching to software development teams.
- Strong communication and presentation skills with the ability to explain AI concepts and tools to technical audiences.
- Experience driving adoption of new technologies, engineering practices or developer tooling within an organization.
- Understanding of software engineering best practices, including code review, testing, security, CI/CD and version control.
- Ability to work effectively with developers, technical leads, architects and engineering managers.
- Strong problem-solving and analytical skills.
- A proactive mindset and genuine interest in emerging AI technologies and their practical application in software engineering.
Responsibilities
- Drive the adoption of AI-assisted software development practices across the internal development organization.
- Introduce, promote and support the use of AI coding assistants such as Claude Code and other developer-focused AI tools.
- Identify development workflows and activities where AI can improve productivity, quality and engineering efficiency.
- Define and maintain best practices, reusable patterns, guidelines and guardrails for the responsible use of AI in software development.
- Establish recommendations for using AI-generated code while maintaining engineering quality, security, maintainability and compliance standards.
- Organize and deliver training sessions, workshops, demos and knowledge-sharing activities for development teams.
- Provide hands-on mentoring and coaching to developers adopting AI-assisted development tools.
- Support developers in integrating AI assistants into their existing development workflows.
- Collect feedback from engineering teams and continuously improve AI adoption strategies and practices.
- Evaluate emerging AI development tools and technologies and assess their potential value for the organization.
- Create documentation, guidelines, examples and reusable resources to support AI adoption.
- Collaborate with technical leaders and engineering teams to define AI adoption objectives and measure progress.
- Help establish metrics and feedback mechanisms to track AI adoption, usage and impact on developer productivity.
- Promote a culture of experimentation, continuous learning and responsible AI adoption across the engineering organization.
- Act as a technical point of reference for AI-assisted development practices and emerging developer AI tooling.