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Open nowPosted 233 days ago

Senior Applied Scientist - Semantics

AuxoAI51 open roles

Where
Bangalore North, Karnataka, India
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Your applicationOpen nowSenior Applied Scientist - SemanticsAuxoAI · Bangalore North, Karnataka, India

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. AuxoAI postings stay open a median of 2 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 233 days ago

AuxoAI median: 2 days open

The posting

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. The ideal candidate will build systems that transform unstructured data into structured knowledge representations, enforce semantic constraints, and enable hybrid symbolic–neural reasoning in production environments. You will play a key role in designing scalable semantic infrastructures that support advanced AI use cases such as GraphRAG pipelines, structured extraction, and agent reasoning workflows. You will work on problems where existing architectures may not be sufficient and will experiment with new approaches that combine machine learning, knowledge graphs, semantic constraints, and classical AI techniques to build reliable, production-grade systems. Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office) 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.

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