Experience
4–12 years
Role Overview
We are seeking experienced software engineers who actively leverage AI coding assistants to deliver high‑quality software faster. In this role, you will design AI‑enabled development frameworks, validation pipelines, and guardrails that ensure AI tools are reliable and productive across the full software development lifecycle (SDLC)—from requirements to deployment.
This is a hands‑on engineering role requiring strong coding depth, sound judgment, and the ability to mentor teams on safe and effective AI‑augmented development practices.
Key Responsibilities
- Use AI coding assistants in day‑to‑day production development to: Build features, Fix bug, Refactor and modernize codebases
- Design and implement AI configuration frameworks that encode: Project conventions, Architectural standards, Security and quality constraints
- Build validation and quality enforcement pipelines, including: Pre‑/post‑execution checks, Automated validation scripts, CI/CD quality gates
- Develop context management and prompting strategies for large, complex codebases
- Balance AI autonomy with human oversight, optimizing iteration speed while maintaining quality and safety
- Debug AI workflows by analysing: Assistant behavior, Configuration settings, Conversation and prompt history
- Mentor team members on: Effective prompting patterns, Reusable AI templates, Safe and scalable automation strategies
Required Skills & Experience
Core Engineering
- Strong hands‑on experience in at least one primary technology stack:
- Java, .NET, JavaScript/TypeScript, or Python
- Proven track record of shipping production software
- Comfortable working across the full SDLC
AI‑Assisted Development (Mandatory)
- Demonstrated impact from AI usage (velocity, quality, refactoring efficiency, etc.)
- Experience debugging and improving AI‑generated outputs
- Claude Certified Architect will be added advantage
- Good to have experience using GitHub Copilot or Claude Code in production projects
Good to Have
- Experience with multiple AI assistants (Cursor, Windsurf, Codex)
- Exposure to building internal developer platforms or tooling
- Experience working in secure or regulated environments
- Familiarity with CI/CD integrations and developer experience (DevEx) initiatives
What Success Looks Like
- Faster feature delivery without compromising quality
- Well‑defined AI guardrails embedded into engineering workflows
- Teams confidently and safely leveraging AI at scale
