Software Engineer (Backend)

About Us:

At Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and product company, we equip enterprises with a comprehensive AI stack to build customized, secure intelligence tailored to their unique business needs.

Driven by advanced Small Language Models and our dynamic metamodel framework, our flagship platforms - Nace Data Intelligence and the Nace SLM Cloud - enable true end-to-end business process automation. The result is transformative ROI: professional services firms using Nace AI are currently recovering 1,000 hours per client engagement, drastically reducing overhead and accelerating delivery.

The work we are doing has a meaningful impact across industries, and every hire at Nace AI plays a critical role in shaping the company’s trajectory. This is a unique opportunity to join a high conviction AI company at an early stage and directly influence its growth.

If building a world-class AI team from the ground up excites you, we’d love to talk.

Role Overview:

As a Full Stack Software Engineer, you will be a pivotal force in developing, deploying, and maintaining the end-to-end infrastructure for our advanced AI systems. This includes designing robust backend services, building intuitive and high-performance user interfaces, and ensuring the seamless integration of LLM-based AI Agents. Your expertise will bridge the gap between frontend user experience, backend scalability, and core AI infrastructure, directly impacting system efficiency, reliability, and user-facing capabilities.

What You'll Do:

- Architect, develop, and maintain scalable full-stack components, including both frontend applications (using modern frameworks like React/Vue/Angular) and robust backend services (leveraging Python/Go/Node.js).

- Design and implement APIs and data pipelines that facilitate the smooth deployment and interaction of sophisticated AI Agents and large-scale data processing workflows.

- Contribute to the development of core AI agent frameworks, focusing on features like tool integration, memory systems, and planning/orchestration modules.

- Develop and implement AI Agent evaluation methodologies and tooling to rigorously test, benchmark, and monitor agent performance, reliability, and safety in production.

- Manage and optimize cloud infrastructure (e.g., AWS, GCP, Azure) to ensure high availability, cost-efficiency, and scalability for both the application layer and the underlying AI compute resources.

- Participate actively in design discussions, code reviews, and cross-team collaboration to deliver high-quality, production-grade solutions across the entire stack.

Minimum Qualifications:

- Bachelor's degree in Computer Science, Computer Engineering, related technical discipline, or equivalent practical experience.

- 3+ years of experience building and maintaining full-stack software infrastructure, with proven expertise in both frontend and backend development.

- Hands-on experience building AI agents, AI agent frameworks/orchestration systems, or complex LLM-powered applications and workflows (e.g., RAG pipelines, multi-agent systems, prompt chaining architectures, or LLM orchestration frameworks).

- Practical knowledge of cloud infrastructure management (e.g., Docker, Kubernetes, Terraform) and CI/CD pipelines.

- Proven expertise in designing, scaling, and optimizing enterprise-grade ML or data-intensive systems.

Preferred Qualifications:

- Master's or Ph.D. degree in Computer Science, Computer Engineering, or a related technical discipline.

- Demonstrated experience developing and managing large-scale distributed systems and high-throughput AI infrastructures.

- Expertise in a modern frontend framework (e.g., React, Vue, Angular) and associated state management libraries.

- Experience in developing and deploying AI Agent Evaluation frameworks (e.g., using tools like LangSmith, Arize, or custom evaluation metrics).

- Demonstrated success building production LLM applications with complex workflows such as autonomous agents, conversational AI systems, or intelligent automation platforms.