Quantizant – AI Harness Engineer

Quantizant is a deep-tech cybersecurity venture focused on one of the biggest technology shifts of our time: the transition to post-quantum security. We help organizations understand where they are vulnerable, map their path to quantum readiness, and modernize security systems before future threats become reality.

We are looking for an AI Harness Engineer to help accelerate software delivery using AI-assisted development tools such as Codex, GitHub Copilot, Claude Code, and related AI engineering assistants. This is a hands-on software engineering role focused on applying AI tools to real development workflows: including coding, testing, debugging, documentation, refactoring, and CI/CD integration, not AI model research.

Our ideal candidate should have strong software engineering fundamentals and experience in commonly used enterprise technologies such as Java, JavaScript, React, Node.js, Python, C/C++, and Go. They should be comfortable reviewing AI-generated code, building reusable AI-assisted development workflows, validating tests, debugging application issues, and ensuring that AI-enabled delivery remains secure, maintainable, and production-ready.

We're looking for builders, not prompt operators. Engineers who think critically about the code they ship (even with AI-assisted development) and are excited to redefine how modern software is built, tested, and delivered would be a great fit. Working across the stack, you'll help teams harness AI responsibly while maintaining the engineering rigor required for enterprise-grade products.

Requirements

• Hands-on software development experience, preferably in enterprise, product, platform, or high-throughput application environments.
• Strong foundation in one or more core programming languages such as Java, Python, JavaScript, C, C++, or Go.
• Experience with backend development, preferably using Java, Node.js, APIs, microservices, or message queue architectures. 
• Experience with frontend or full-stack development using technologies such as React, Vue, Vite, JavaScript, or modern web application frameworks. 
• Good understanding of Git, CI/CD, APIs, testing, debugging, and software engineering best practices.
• Able to use AI-assisted development tools such as Codex, Codex CLI, GitHub Copilot, Claude Code, and other AI coding assistants. 
• Able to independently review, validate, and improve AI-generated code rather than relying on it blindly.
• Strong problem-solving, debugging, and systems-thinking skills. 
• Comfortable working in fast-moving engineering environments where requirements may evolve quickly.

Good to Have

• Exposure to Kubernetes, SaaS platforms, microservices, distributed systems, virtualization, or cloud-native infrastructure. 
• Experience with automated testing frameworks and test generation workflows.
• Exposure to DevOps, platform engineering, deployment pipelines, or infrastructure automation.
• Experience supporting enterprise, banking, financial services, cybersecurity, or regulated environments. 
• Familiarity with monitoring or observability tools such as InfluxDB, Grafana, logging dashboards, or performance monitoring scripts.
• Knowledge of secure coding practices and engineering controls.
• Experience working with databases, messaging systems, APIs, or integration-heavy enterprise platforms.