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Mid-level GenAI role, metro location, strong company brand, and popular skillset increase applicant competition.
GenAI engineering skills are specialized but widely applicable across industries, so moderate cross-industry transferability.
Explicit 5–9 years plus specific GenAI frameworks, vector DBs, AWS, and model fine-tuning make filters stringent.
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Architect and build production-ready multi-agent AI ecosystems using frameworks like LangChain and LangGraph.
Design and optimize AI orchestration including complex routing, intent recognition, multi-step planning, and RAG pipelines.
Lead initiatives to ensure AI workflows are scalable, secure, compliant with global data privacy regulations, and fine-tune foundational models with enterprise data.
5 to 9 years of professional experience with at least 1 to 3 years focused on Generative AI development.
Advanced proficiency in Python programming and software development best practices.
Hands-on experience with GenAI frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or Hugging Face and vector databases like Pinecone or ChromaDB.
Expertise in deploying AI solutions on AWS cloud and familiarity with REST APIs, microservices architecture, and SAFe Agile methodologies.
Experienced in leading or developing complex multi-agent AI systems and AI orchestration at scale.
Skilled in integrating advanced prompt engineering and explainability tools (e.g., Langfuse, LangSmith) to ensure transparent and safe model outputs.
Capable of working in a dynamic, regulated environment requiring compliance with global data privacy and governance standards.