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Mid-level generalist title and metro location but niche LLM/agent requirements reduce applicant density.
Agentic AI and vector-database expertise reduce cross-industry transferability despite general full-stack skills.
Explicit 5+ years plus required LLM, Kubernetes, and enterprise security skills create strict filtering.
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Design, develop, and deliver production-grade full-stack applications for agentic AI systems, including backend services for agent runtimes, and frontend interfaces for AI agent interaction.
Integrate large language models (LLMs), tools, APIs, and data platforms to build scalable services and connect business workflows.
Contribute to architectural decisions, engineering standards, code reviews, performance optimization, and secure enterprise integrations.
5+ years of professional software engineering experience with full-stack application delivery in production.
Experience with agentic AI design patterns (ReAct, tool-use, multi-step reasoning) and familiarity with agent frameworks like LangChain.
Knowledge of vector databases and retrieval-augmented generation (RAG), Kubernetes or managed container services, and enterprise security patterns including authentication and IAM.
Degree in Computer Science, Engineering, or related field (or equivalent experience).
Experienced in cross-functional product teams collaborating with AI/ML engineers and DevOps for productizing AI prototypes.
Strong background in integrating LLMs and agent frameworks into business workflows and internal tools.
Technically versatile engineer comfortable with complex, evolving AI-native software architectures and responsible for scalable, maintainable production systems.