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Niche RAG/GraphRAG specialization and non-metro Kochi location lower candidate competition.
Specialized LLM, GraphRAG, and ontology expertise reduces cross-industry transferability.
Multiple mandatory technical requirements and explicit 6+ years experience create high filter strictness.
Develop and implement advanced AI/ML features including RAG pipeline integration, prompt tuning, and graph-aware retrieval techniques.
Deliver full orchestration integration for the platform’s cookbook-based orchestration component, enhancing query generation and agent routing.
Collaborate with AI Architects, Backend Engineers, and KG validation teams to evolve knowledge-graph schemas and expand golden Q&A datasets for LLM semantic grounding.
6+ years in AI engineering with strong Python programming skills.
3+ years practical experience implementing RAG/GraphRAG pipelines including vector store integration and orchestration.
Experience with SPARQL, ontology mapping, semantic disambiguation, and building or validating golden Q&A datasets.
B.Tech or MCA in Computer Science, AI/ML, or a related field, or equivalent practical experience.
Experienced in designing and tuning LLM applications operating within capped, governed dataset constraints rather than open-ended tuning.
Skilled in integrating graph-based dynamic query generation replacing template-based SPARQL systems in regulated or enterprise settings.
Ability to produce technical design documents for roadmap or upgrade strategies alongside code implementations.