Software Engineer

SourcingXPress; Hyderabad; On-site; INR 1,500,000 - 4,500,000; Full time

Job summary

Arionix Hyderabad is seeking a senior data/knowledge engineer to design and evolve the enterprise context graph, capturing entities, relationships, and metadata that reflect how the organization operates.

You will build ingestion pipelines from diverse sources, implement entity resolution across systems, and architect a graph + vector storage layer for fast retrieval. You will collaborate with security, governance, and applied AI/ML teams to improve grounding and provenance.

Qualifications

• 5+ years in data engineering, knowledge engineering, or backend systems.
• Hands-on production data pipelines experience at scale.
• Strong graph databases and graph query languages.
• Ontology/taxonomy design experience is valued.
• Experience with entity resolution/record linkage.
• Proficient in Python and orchestration tools (Airflow, Dagster).
• Knowledge of data governance and provenance tracking.
• Familiarity with vector databases and hybrid retrieval for RAG apps.
• Ability to operate in ambiguity and define schema/pipeline from scratch.

Responsibilities

• Design and evolve the ontology/schema for an enterprise context graph, including entities, relationships, and metadata.
• Build ingestion pipelines to extract structured and unstructured context from diverse sources.
• Implement entity resolution and deduplication across disparate sources.
• Architect storage/query layer (graph database + vector store) for low-latency retrieval.
• Create APIs/retrieval interfaces for LLM agents and internal tools to query the graph.
• Establish freshness, versioning, provenance, and data-trust mechanisms.
• Collaborate with security and governance teams to enforce access control.
• Define quality metrics and instrument pipelines to catch regressions.
• Work with applied AI/ML teams to improve grounding and reduce hallucination.

Skills

Graph databases

Python

ETL pipelines

NLP/LLM

Entity resolution

Airflow/Dagster

Data governance

Knowledge graphs

Tools

Neo4j

Cypher/Gremlin

SPARQL

Spacy/LLM tools

Job description

• 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.

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

Job Description

• 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).
• Strong 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.