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Niche LLM and knowledge-graph skillset plus non-tier1 employer reduces candidate competition.
Specialized LLM and knowledge-graph engineering skills transfer across AI projects but require niche expertise.
Multiple mandatory specialized LLM, graph, and tool-specific skills create strict technical shortlisting filters.
Develop and implement LLM applications using Amazon Bedrock and advanced prompt engineering techniques.
Build and integrate knowledge graphs with RAG patterns, including graph-augmented generation and subgraph retrieval for improved context injection.
Design and maintain AI-driven data pipelines and Text2SQL solutions utilizing Python/TypeScript and frameworks like LangChain or LlamaIndex.
Experience with Amazon Bedrock and prompt engineering for LLM applications.
Proficiency in Python and/or TypeScript programming languages.
Hands-on knowledge of knowledge graph technologies including RDF, OWL, SKOS, and graph databases such as Neptune or Neo4j.
Work Experience Required: Not explicitly mentioned in the JD.
Demonstrated expertise in building and deploying generative AI and knowledge engineering solutions at scale.
Familiar with designing ontologies, taxonomy modeling, and knowledge representations for complex data ecosystems.
Experienced in implementing RAG patterns and integrating AI frameworks such as LangChain or LlamaIndex in production environments.