Company: Arionix Website: Visit Website Business Type: Startup Company Type: Product Business Model: B2B Funding Stage: Pre-seed Industry: Information Technology Salary Range: ₹ 15-45 Lacs PA What You'll Do Design and evolve the ontology/schema for the enterprise context graph — entities (people, teams, systems, products, projects, customers, policies), relationships, and metadata that reflect how the organization operates.

Build ingestion pipelines that extract structured context from source systems (CRM, ticketing, HRIS, wikis, code repos, data warehouses) and unstructured context (documents, Slack/Teams, meeting transcripts) using a mix of ETL, NLP, and LLM-based extraction.

Implement entity resolution and deduplication so the same person, system, or concept is correctly linked across disparate sources.

Architect the storage and query layer (graph database, hybrid graph + vector store) for low-latency retrieval by both applications and AI agents.

Build APIs and retrieval interfaces (including MCP servers or similar protocols) so LLM agents and internal tools can query the graph for grounded, up-to-date context.

Establish freshness, versioning, and provenance mechanisms — every fact in the graph should be traceable to a source and timestamp, and stale or conflicting data should be surfaced, not silently trusted.

Partner with security and data governance teams to enforce access control at the entity/relationship level, since context graphs often surface sensitive cross-departmental information.

Define and track quality metrics for the graph — coverage, accuracy, staleness, resolution rate — and instrument pipelines to catch regressions.

Collaborate closely with applied AI/ML teams building agents and copilots that consume the graph,



iterating based on what actually improves grounding and reduces hallucination.

What We're Looking For 5+ years in data engineering, knowledge engineering, or backend systems, with real experience shipping production data pipelines at scale.

Hands-on experience with graph databases (Neo4j, Amazon Neptune, TigerGraph, or similar) and graph query languages (Cypher, Gremlin, SPARQL).

Solid understanding of ontology and taxonomy design — RDF/OWL familiarity is a plus, but practical schema judgment matters more than academic purity.

Experience building entity resolution / record linkage systems (rule-based, ML-based, or LLM-assisted).

Solid Python (or equivalent) skills, with experience orchestrating pipelines (Airflow, Dagster, or similar) and working with both batch and streaming data.

Practical experience integrating LLMs for extraction and structuring — turning unstructured text into structured graph facts, including handling ambiguity and low-confidence extractions.

Familiarity with vector databases and hybrid retrieval (combining graph traversal with semantic search) for RAG-style applications.

Understanding of data governance, access control, and provenance tracking in a multi-source enterprise environment.

Comfort operating in ambiguity — this role often means defining the schema and the pipeline architecture from scratch, not implementing a pre-built spec. Nice to Have Experience with Model Context Protocol (MCP) or building tool/context interfaces for LLM agents.

Background in semantic web technologies, knowledge representation, or search relevance engineering.

Prior experience at a company that built an internal "single source of truth" system (data catalog, CMDB, master data management).

Experience with change-data-capture (CDC) pipelines for keeping a graph continuously fresh.

📌 Software Engineer (Hyderabad)
🏢 Sourcingxpress
📍 Hyderabad