Overview
Software Engineer - AI Assisted Development
Responsibilities
* 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, and 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, and 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, and 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.
Amaris Consulting is proud to be an equal-opportunity workplace. We are committed to promoting diversity within the workforce and creating an inclusive working environment. For this purpose, we welcome applications from all qualified candidates regardless of gender, sexual orientation, race, ethnicity, beliefs, age, marital status, disability, or other characteristics.
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