AI Software Systems Engineer / Full-Stack Developer

Role Overview

Bennet & Bernard is looking for a hands-on AI Software Systems Engineer / Full-Stack Developer to build, test, deploy, and improve AI-enabled software tools and internal company systems.

This is a practical builder role. The person must be able to code directly, test their own work, debug issues, learn new tools quickly, and deliver working software.

The company is currently building internal systems and will gradually scale suitable tools for wider use.Purpose of the Role

The role is to turn business ideas and research needs into working software, AI-enabled tools, dashboards, automation workflows, knowledge systems, and API-connected platforms.Key Responsibilities1. Software Development

· Build full-stack software tools for internal company use.

· Create prototypes, MVPs, dashboards, automation tools, and production-ready systems.

· Develop clean, maintainable, scalable, and testable code.

· Work across frontend, backend, AI, cloud, database, and testing layers.2. Frontend and Backend Technology Stack

The candidate should understand modern frontend and backend development and should be comfortable learning new tools when required.

· Frontend: React, Next.js, JavaScript, TypeScript, HTML, CSS, Tailwind CSS, shadcn/ui, responsive UI, component-based architecture, frontend debugging, and frontend testing.

· Backend: Python, Rust, C++, Node.js, API development, REST APIs, databases, authentication, permissions, backend workflow logic, third-party APIs, data processing, and integrations.

· Additional: Java is a strong plus, especially for testing purposes. Shell scripting, Git, GitHub, CLI / command-line workflows, and basic CI/CD knowledge are also useful.3. AI-Enabled Systems Development

· Build AI-enabled systems using AI APIs, LLMs, agentic workflows, automation logic, and retrieval systems.

· Work with OpenAI, Claude, Gemini, or similar AI / LLM platforms.

· Build AI workflows involving tool-calling, memory, retrieval, summarization, classification, and automation.

· Connect AI tools with company documents, databases, APIs, internal workflows, and business systems.

· Use AI-assisted coding tools responsibly and verify AI-generated code before using it.4. Cloud-Based Platforms and Infrastructure

The company’s main cloud and infrastructure focus is:

· AWS

· Oracle Cloud

· Supabase

· Cloudflare

The candidate should be able to build, deploy, and maintain cloud-based applications, backend services, databases, storage layers, authentication systems, API-connected tools, and automation workflows.

Knowledge of Azure, Google Cloud, Firebase, or similar platforms is a plus. Flexibility to learn new cloud tools quickly is highly valued.

· Useful infrastructure knowledge includes cloud databases, cloud storage, serverless functions, identity and access management, deployment pipelines, monitoring, logging, GitHub workflows, CLI workflows, and basic DevOps.5. Testing and Quality Control

· Test frontend, backend, API, database, workflow, and AI-enabled systems before release.

· Write and run unit tests, integration tests, frontend tests, API tests, and workflow tests.

· Test AI workflows for reliability, edge cases, hallucinations, output quality, and failure modes.

· Debug issues and improve system performance, reliability, and usability.

· Use tools such as Pytest, Jest, Playwright, Cypress, Postman, or similar tools.

· Java or Java-based testing knowledge is a strong plus.6. Research, Learning, and System Design

· Research new technologies, AI tools, cloud services, and software architectures.

· Compare technical options and recommend practical solutions.

· Prepare technical architecture for new internal systems.

· Translate business requirements into technical specifications and development plans.

· Understand how separate tools can connect into one larger company-wide software ecosystem.

· Learn unfamiliar tools quickly and apply them in practical use cases.Required Technical Skills

· Full-stack software development.

· Frontend development using React and related technologies.

· Backend development using Python, Rust, C++, Node.js, APIs, databases, authentication, and workflow logic.

· Practical AI-enabled software development.

· Cloud deployment and infrastructure knowledge.

· API integrations and system connectivity.

· Testing, debugging, and quality control.

· Git, GitHub, CLI / command-line workflows, and basic CI/CD understanding.AI Knowledge

Practical AI knowledge is required. The candidate should understand or be able to work with:

· AI / LLM APIs such as OpenAI, Claude, Gemini, or similar.

· Prompt engineering and AI workflow design.

· Tool-calling workflows and agentic AI workflows.

· RAG, retrieval systems, embeddings, vector databases, and AI memory systems.

· AI research assistants, AI automation workflows, and AI-system integrations.

· MCP / Model Context Protocol for connecting AI tools with systems, files, APIs, and databases.

· Obsidian for knowledge structuring, research notes, documentation, and connected knowledge workflows.

· AI-assisted coding tools such as Claude Code, Cursor, Google Antigravity, or similar tools.

· Responsible AI-code review, debugging, documentation, and testing.Required Experience

· Preferably 3-5 years of recent hands-on software development experience.

· Experience building full-stack applications (Web-app, IOS and Android) (native apps is a plus point), dashboards, internal tools, automation systems, or workflow platforms.

· Practical experience with frontend and backend development.

· Experience with API calling, databases, authentication, integrations, and cloud deployment.

· Exposure to AI-enabled software systems and AI APIs.

· Ability to build prototypes without depending on a large engineering team.

· Ability to test, debug, improve, and maintain their own work.

· Ability to learn unfamiliar tools quickly and apply them practically.Required Mindset

· Hands-on and willing to code directly.

· Practical, curious, and solution-oriented.

· Strong in AI knowledge and open to AI-assisted development.

· Able to learn new tools quickly and try new approaches.

· Comfortable working with unclear or changing requirements.

· Able to build, test, deploy, and improve software systems.

· Focused on delivery, not only research or theory.

· Comfortable working directly with research teams and management.What We Are Not Looking For

· A pure project manager.

· Someone who only advises but does not build.

· A developer who refuses to work with AI tools.

· Someone with only outdated software experience.

· Someone who only knows one narrow technology stack and is unwilling to learn.

· Someone who cannot test or debug their own work.

· Someone who needs a large team before they can start building.

· Someone who is negative or dismissive about AI.