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Remote role with broad SDLC requirements increases competition, while niche AI-tool training reduces applicant density.
High because hands-on AI-tool and training experience is specialized and not easily transferable without practice.
Multiple explicit multi-year requirements across AI tools, CI/CD, and training create strict shortlisting filters.
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Design, deliver, and continuously update hands-on AI training curriculum covering Development, QA, DevOps, and AI-assisted software delivery.
Review trainees’ code and exercises, providing practical, direct feedback to improve their skills.
Engage hands-on with emerging AI tools and technologies to ensure training remains relevant and practical.
6–9 years of hands-on software engineering experience covering full SDLC including Development, QA, and DevOps.
2+ years daily hands-on use of AI coding tools like Claude Code or GitHub Copilot.
3+ years CI/CD pipeline configuration experience, with 6–12 months AI-driven CI/CD automation knowledge.
2+ years experience in coaching, mentoring, or delivering technical training/workshops.
Strong generalist with deep hands-on expertise across Development, QA, DevOps, and full software development lifecycle rather than specialization.
Experienced in practical application and delivery with AI-assisted software tools, not just theory or conceptual knowledge.
Capable of independently designing training content and delivering it collaboratively, with a patient coaching style suitable for varied experience levels.