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Mid-level, popular fullstack title plus hybrid working increases competition, though LLM/RAG specialization narrows it.
Fullstack engineering skills are transferable, but agentic AI, vector DB and RAG require domain-specific experience.
Mandatory 5+ years plus required LLM, RAG, LangChain, Kubernetes, and enterprise security skills raise strictness.
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Design and deliver full-stack applications integrating LLMs, tools, APIs, and data platforms for agentic AI systems.
Implement backend services and frontend interfaces for AI agent runtimes, tool execution, memory layers, and workflow monitoring.
Contribute to architectural decisions, productionize AI prototypes, optimize performance, and collaborate with cross-functional teams including Product, AI/ML engineers, and DevOps.
5+ years of professional software engineering experience.
Experience delivering production-grade full-stack applications.
Exposure to agentic AI design patterns (e.g., ReAct, tool-use, multi-step reasoning) and familiarity with agent frameworks like LangChain.
Experience with Kubernetes or managed container services and understanding of authentication, IAM, and enterprise security patterns.
Experienced in building scalable, secure, enterprise-grade AI-powered internal tools or developer productivity tools.
Able to operate effectively in cross-functional product teams focusing on AI-native software architectures.
Comfortable with evolving technical domains involving vector databases, retrieval-augmented generation, and integration of LLMs into business workflows.