Auxo AI - Senior Applied Scientist - Semantic Systems

Auxo AI; Gurugram District; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

Auxo AI is seeking a Senior Applied Scientist to design and deploy structured knowledge systems enabling reliable, schema-grounded AI and agent reasoning. This role sits at the intersection of LLMs, knowledge graphs, semantic architectures, and hybrid retrieval systems.

You will design schema-guided information extraction systems, build multi-stage pipelines for nested entities, and develop ontology-driven architectures using LinkML, OWL/SHACL, and RDF representations.

Qualifications

• Strong Python engineering skills with emphasis on data validation and schema integrity.
• Experience with knowledge graphs or ontology-driven architectures.
• Hands-on experience with structured extraction techniques and semantic data models.

Responsibilities

• Design schema-guided information extraction systems using zero-shot and few-shot prompting and constrained decoding.
• Develop multi-stage extraction pipelines handling nested entities and cross-document relationships.
• Build ontology-driven systems with frameworks like LinkML, OWL, SHACL, and RDF representations.
• Design and optimize entity resolution using blocking, embedding similarity, and rule-based matching.
• Develop ontology alignment and graph embedding models such as Node2Vec or TransE-style approaches.
• Create hybrid retrieval architectures combining dense and sparse retrieval with graph traversal.
• Build validation systems enforcing schema conformance and reducing hallucinations.
• Integrate structured knowledge systems into GraphRAG pipelines, agent planning, and tool-selection workflows.
• Deliver production-grade semantic systems with targets for latency, scalability, and reliability.
• Strong experience in knowledge graphs and ontology-driven architectures.
• Hands-on with grammar-constrained decoding, JSON schema enforcement, or AST parsing.
• Experience building entity resolution beyond simple embedding methods.
• Experience with SPARQL or Cypher and optimizing graph queries.
• Familiarity with RDF, OWL, or property graph data models.

Skills

Python

Knowledge graphs

Graph databases

SPARQL

Cypher

Tools

LinkML

OWL/SHACL

RDF

Job description

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 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.

(ref:hirist.tech)