{"id":2920683,"url":"https://alion.io/job/auxoai-senior-applied-scientist-semantics-2","title":"Senior Applied Scientist - Semantics","company":{"id":46892,"name":"AuxoAI","domain":"auxoai.com","url":"https://alion.io/company/auxoai","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":{"grade":"A","score":89,"open_postings":9,"ghost_share":0,"stale_share":0.222,"repost_share":0,"time_to_fill_p50_days":76,"computed_at":"2026-10-25T05:49:26Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":38},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"GraphRAG","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Structured Outputs","optional":false}],"status":"live","first_seen_at":"2026-03-05T00:00:00Z","employer_posted_date":"2026-03-05","last_verified_at":"2026-10-25T19:16:32Z","board_verified":true,"closed_at":null,"days_open":234,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":234},"description":"AuxoAI is seeking a Senior Applied Scientist to design and deploy structured knowledge systems that enable reliable, schema-grounded AI and agent reasoning.\nThis 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.\nYou 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.\nYou 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.\nLocation - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)\nResponsibilities:\nDesign 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.\nDevelop recursive or multi-stage extraction pipelines capable of handling nested entities, hierarchical structures, and cross-document relationships.\nBuild 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.\nDesign and optimize entity resolution algorithms using techniques such as blocking strategies, embedding similarity, and rule-based matching.\nDevelop ontology alignment techniques and graph embedding models such as Node2Vec or TransE-style approaches where appropriate.\nDesign hybrid retrieval architectures combining dense vector retrieval, sparse retrieval techniques, and graph traversal algorithms such as BFS, DFS, path ranking, and neighborhood expansion.\nBuild validation systems that enforce schema conformance, detect semantic inconsistencies, and reduce hallucinated or invalid structured outputs.\nIntegrate structured knowledge systems into GraphRAG pipelines, agent planning frameworks, and tool-selection workflows.\nDeliver production-grade semantic systems with clear targets for latency, scalability, reliability, and data integrity.\nRequirements\n5+ years of experience building production AI or machine learning systems.\nStrong experience designing and implementing knowledge graphs or ontology-driven architectures.\nHands-on experience implementing structured extraction techniques, including grammar-constrained decoding, JSON schema enforcement, or AST-style parsing approaches.\nExperience building entity resolution systems beyond simple embedding similarity methods.\nExperience working with graph query languages such as SPARQL or Cypher and optimizing graph query performance.\nFamiliarity with RDF, OWL, or property graph data models and semantic data architectures.\nStrong Python engineering skills, with emphasis on data validation, schema integrity, and system reliability.\nExperience designing hybrid symbolic and neural AI systems.\nNice to Have:\nExperience implementing graph algorithms such as PageRank, community detection, or shortest-path algorithms for reasoning chains.\nExperience building graph-enhanced retrieval systems such as GraphRAG.\nExperience designing compositional semantic extraction pipelines.\nExperience implementing reasoning engines or rule-based inference systems.\nExperience benchmarking and evaluating structural extraction accuracy and consistency.","description_format":"text","description_chars":3755,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Agents","Decision Intelligence","AI Consulting & Integration","Data 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