Software Engineer - AI Assisted Development
Project Summary
We are looking for a Software Engineer to lead the adoption of AI-assisted coding practices across the organization. This role combines hands-on engineering, enablement, and governance—defining how AI is used in software development while actively supporting teams in building their first applications using AI-assisted workflows. You will act as both a coach and a quality gate, ensuring teams leverage AI effectively without compromising engineering standards.
Main Responsibilities / Objectives
AI-Assisted Development Practices
- Define and standardize best practices for AI-assisted coding across teams.
- Establish clear guidelines for when and how to use AI tools (generation, refactoring, testing, documentation).
- Create reusable playbooks, patterns, and prompt libraries for effective AI usage.
- Promote responsible usage with strong human-in-the-loop validation and traceability.
Hands-On Enablement & App Development
- Partner with teams to build their first applications using AI-assisted coding (\ "vibe coding\").
- Review and guide early implementations to ensure quality, maintainability, and alignment with architecture standards.
- Act as a technical coach, helping engineers translate AI-generated outputs into production-ready solutions.
- Identify common pitfalls and turn them into reusable best practices and guardrails.
Tooling & Integration
- Evaluate and integrate AI coding tools (e.G., GitHub Copilot, Cursor IDE).
- Embed AI into the development lifecycle (IDE, CI/CD, code reviews).
- Build internal tooling or wrappers to: Standardize usage patterns, Enforce guardrails, Capture metrics and insights
Engineering Quality & Governance
- Define quality standards for AI-generated code (testing, security, performance).
- Establish review processes adapted to AI-assisted development.
- Ensure compliance with enterprise requirements (security, licensing, data privacy).
- Prevent anti-patterns such as: Blindly trusting generated code, Inconsistent architectures, Duplication or technical debt at scale
Developer Experience & Enablement
- Train teams on effective AI-assisted workflows.
- Create onboarding materials, demos, and real-world examples.
- Scale knowledge across teams to ensure consistent adoption.
Continuous Improvement
- Run pilots and experiments with new AI tools and workflows.
- Measure impact on productivity, quality, and delivery speed.
- Continuously refine practices based on real usage and feedback.
Expected Deliverables
- A standardized framework for AI-assisted development.
- Successfully delivered first applications built using AI-assisted workflows, with documented learnings.
- Code reviews and guidance that elevate team output quality.
- Internal tools, templates, and prompt libraries.
- Training materials and onboarding sessions.
- Measurable improvements in developer productivity and code quality.
Required Skills
- Experience: 5–10 years of experience in software engineering.
- Proven experience building production-grade applications.
- Hands-on experience using AI coding tools in real projects.
- Experience mentoring or guiding other engineers.
Software Engineering
- Strong programming skills (TypeScript/JavaScript, Python, or similar).
- Experience with modern architectures (APIs, distributed systems, frontend frameworks).
- Strong foundation in testing, maintainability, and performance.
AI-Assisted Development
- Strong understanding of: Prompt engineering for code generation, Limitations and risks of LLM outputs, Validation and review patterns
DevOps & Tooling
- Familiarity with CI/CD, code quality tooling, and developer workflows.
Governance & Security
- Understanding of secure coding, compliance, and licensing considerations.