AI Full-Stack Engineer — Agentic AI & Automation
Summary
Build AI-powered apps and agents using LLMs, RAG, and automation; full-stack role covering frontend, backend, APIs, and deployment.
We are seeking an AI Full-Stack Engineer to design, develop, and maintain AI-powered applications, intelligent agents, and automation solutions. The ideal candidate has strong full‑stack development expertise combined with hands‑on experience in Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), prompt engineering, APIs, and workflow automation. This role involves collaborating with business teams to build scalable, production‑ready AI solutions that enhance operational efficiency.
Key Responsibilities
- Design, develop, and maintain AI agents and AI‑powered business applications.
- Build and maintain frontend interfaces, backend services, APIs, dashboards, and internal tools.
- Integrate AI solutions with databases, documents, CRMs, emails, APIs, and enterprise systems.
- Develop RAG‑based applications, knowledge assistants, document intelligence, and workflow automation solutions.
- Optimize AI performance through prompt engineering, testing, evaluation, monitoring, and feedback loops.
- Convert AI prototypes into secure, scalable, and production‑ready applications.
- Analyze business processes and identify opportunities for AI‑driven automation.
- Document system architecture, workflows, technical designs, and user guides.
- Ensure application security, authentication, role‑based access control, and data privacy best practices.
Qualifications
- Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related field (or equivalent practical experience).
- Strong experience in full‑stack software development.
- Proficiency in backend technologies such as Python, Node.js, Java, or similar.
- Experience with frontend frameworks such as React, Angular, Vue.js, or Next.js.
- Hands‑on experience with REST APIs, databases, Git, Docker, and deployment workflows.
- Practical knowledge of LLMs, AI agents, prompt engineering, RAG, embeddings, and vector databases.
- Experience integrating third‑party APIs and enterprise systems.
- Understanding of cloud platforms (Azure, AWS, or GCP) and AI platforms such as OpenAI, Azure OpenAI, Anthropic, or Gemini is preferred.
- Familiarity with AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or CrewAI is a plus.