GEN AI Engineer

. Design and develop AI pipelines for document parsing, information extraction, and text understanding using LLMs and NLP techniques.
. Develop and refine algorithms for knowledge graph construction, entity linking, and relationship extraction from text corpora.
. Implement GenAI systems for summarization, semantic search, and contextual reasoning using retrieval-augmented generation (RAG).
. Work with vector databases, embedding generation, and model fine-tuning for domain-specific applications.
. Build and maintain scalable APIs and backend services to integrate GenAI capabilities with enterprise applications.
. Design and optimize Neo4j-based knowledge graphs to represent and query structured relationships from unstructured data.
. Collaborate with data scientists, AI/ML engineers, and software teams (frontend/backend) to integrate GenAI features into production.
. Evaluate and apply frameworks such as LangChain, LlamaIndex, Hugging Face, and OpenAI APIs for document intelligence workflows.
. Develop algorithms for semantic search, reasoning, and document-to-graph alignment using graph embeddings and transformer architectures.
. Ensure data quality, governance, and compliance with enterprise security and information management standards.
. Document research findings, maintain technical design specifications, and support continuous model improvement.

The GenAI Engineer will be responsible for designing, developing, and deploying AI-driven systems focused on document parsing, context understanding, and knowledge graph construction. The role combines expertise in generative AI models, large language model (LLM) frameworks, and graph-based data representation. The engineer will build scalable document understanding pipelines, extract relationships from unstructured data, and construct knowledge graphs (using technologies such as Neo4j) to enable intelligent information retrieval and reasoning across enterprise datasets