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Mid-level GenAI engineer with broad full-stack requirements increases candidate competition.
Specialized GenAI and agent-framework experience moderately reduces cross-industry transferability.
Explicit 3–5 years plus mandatory GenAI, Python, cloud, and agent-framework experience restricts shortlisting strongly.
Design, build, and deploy full-stack AI-powered applications including multi-agentic systems and end-to-end GenAI workflows integrated with enterprise data and APIs.
Own technical decisions on architecture, integration, and build production-grade GenAI solutions with minimal supervision alongside senior AI architect.
Develop and maintain RAG pipelines and AI agent frameworks, ensuring production readiness with best practices like CI/CD and testing.
3–5 years of software or AI engineering experience.
Strong Python proficiency with production-level coding skills.
Experience building and shipping end-to-end GenAI or LLM-powered applications and full-stack development including backend APIs and frontend.
Cloud platform experience (AWS, Azure, or GCP) and familiarity with AI agent frameworks (LangChain, LangGraph, LlamaIndex or equivalent).
Demonstrated ability to independently translate business requirements into scalable GenAI solutions and own failures end-to-end.
Experience working in dynamic environments such as startups or consulting, comfortable driving architecture and integration decisions.
Skillful in debugging, problem-solving, and raising technical standards through best practices in CI/CD, testing, and observability.