About the Role :

AuxoAI is seeking a Senior Applied Scientist to design and deploy structured knowledge systems that enable reliable, schema-grounded AI and agent reasoning. This role sits at the intersection of large language models, knowledge graphs, semantic architectures, and hybrid retrieval systems.

Responsibilities :

- Design schema-guided information extraction systems using zero-shot and few-shot structured prompting, constrained decoding approaches such as JSON schema enforcement or grammar-based decoding, and function-calling or tool-driven extraction techniques.

- Develop recursive or multi-stage extraction pipelines capable of handling nested entities, hierarchical structures, and cross-document relationships.

- Build ontology-driven systems using frameworks such as LinkML, OWL, SHACL, or similar schema modeling tools, and implement knowledge representations using RDF triples or labeled property graphs.

- Design and optimize entity resolution algorithms using techniques such as blocking strategies, embedding similarity, and rule-based matching.

- Develop ontology alignment techniques and graph embedding models such as Node2Vec or TransE-style approaches where appropriate.

- Design hybrid retrieval architectures combining dense vector retrieval, sparse retrieval techniques, and graph traversal algorithms such as BFS, DFS, path ranking, and neighborhood expansion.

- Build validation systems that enforce schema conformance, detect semantic inconsistencies, and reduce hallucinated or invalid structured outputs.

- Integrate structured knowledge systems into GraphRAG pipelines, agent planning frameworks, and tool-selection workflows.

- Deliver production-grade semantic systems with clear targets for latency, scalability, reliability, and data integrity.

Requirements:

- 5+ years of experience building production AI or machine learning systems.

- Strong experience designing and implementing knowledge graphs or ontology-driven architectures.

- Hands-on experience implementing structured extraction techniques, including grammar-constrained decoding, JSON schema enforcement, or AST-style parsing approaches.

- Experience building entity resolution systems beyond simple embedding similarity methods.

- Experience working with graph query languages such as SPARQL or Cypher and optimizing graph query performance.

- Familiarity with RDF, OWL, or property graph data models and semantic data architectures.

- Strong Python engineering skills, with emphasis on data validation, schema integrity, and system reliability.

- Experience designing hybrid symbolic and neural AI systems.

Nice to Have :

- Experience implementing graph algorithms such as PageRank, community detection, or shortest-path algorithms for reasoning chains.

- Experience building graph-enhanced retrieval systems such as GraphRAG.

- Experience designing compositional semantic extraction pipelines.

- Experience implementing reasoning engines or rule-based inference systems.

- Experience benchmarking and evaluating structural extraction accuracy and consistency.

Note :

Given the urgency of the role, we are currently prioritizing candidates who can join immediately or within 2 weeks.