AI Engineer (Solution)

We are looking for a versatile and results-driven Full Stack AI Engineer to design, build, and deploy intelligent business applications. In this role, you will bridge the gap between core AI capabilities and production-ready software—leveraging existing Large Language Models (LLMs) and modern web frameworks to solve complex enterprise challenges.

The ideal candidate is a pragmatic software engineer who excels at rapid prototyping, full-stack development, and orchestrating autonomous AI agents. You should be comfortable building the "brain" of the application (RAG, Agentic orchestration) as well as the "body" (FastAPI backends, Next.js frontends, and Dockerized infrastructure).

Key Responsibilities

• Agentic Orchestration & Skills: Develop and configure autonomous AI agents capable of multi-step reasoning. Build custom "skills" and tools that allow agents to interact with external APIs, databases, and proprietary software via the Model Context Protocol (MCP).
• Applied AI & RAG Integration: Architect and maintain Retrieval-Augmented Generation (RAG) pipelines. Optimize vector embeddings and data retrieval strategies to securely connect LLMs with enterprise data for high-accuracy outputs.
• Full-Stack Application Development: Design and implement both frontend and backend components using Next.js and FastAPI. Build responsive user interfaces and robust core business logic to support AI-powered features.
• Custom Software Engineering: Write clean, maintainable Python and TypeScript code to handle complex data transformations, custom node development, and system integrations that extend beyond standard library capabilities.
• Infrastructure & Security: Package and manage AI applications using Docker for reliable distribution. Implement strict network isolation and volume mapping to ensure secure, standardized data access for locally hosted agents and services.
• System Integration & APIs: Design and develop scalable RESTful APIs. Ensure smooth integration between AI models, internal data sources, and user-facing applications using Git-based collaborative workflows.

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Minimum Qualifications

• Education: Bachelor’s degree in Computer Science, Software Engineering, AI, or a related field. (1–2 years of experience preferred; high-potential fresh graduates are welcome).
• Applied AI Expertise: Proven experience utilizing LLMs to drive business value, with a deep understanding of Prompt Engineering, Tool/Function Calling, and RAG architectures.
• Modern AI Stack: Hands-on experience with AI development frameworks such as LangChain, vector databases, and agentic platforms (e.g., OpenClaw).
• Programming Proficiency: Strong experience in Python (FastAPI) and JavaScript/TypeScript (Next.js/React).
• DevOps & Containerization: Working knowledge of Linux environments (CLI), Docker containerization for isolating agent environments, and Git for version control.
• System Architecture: Solid understanding of REST API development, software design principles, and basic software testing practices (unit testing).

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Nice-to-Haves

• Advanced Orchestration: Familiarity with the Model Context Protocol (MCP) for standardizing secure connections between AI models and internal data.
• Deployment Experience: Experience deploying services using container orchestration platforms in on-premise or private cloud infrastructure.
• Workflow Automation: Experience with backend automation logic and state/memory management for long-running AI tasks.
• UI/UX Design: Ability to design intuitive interfaces for complex AI-driven workflows.