Graph RAG - Knowledge Engineer

Proof of Skill; Hyderabad; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Chryselys in Hyderabad, India, is seeking a senior software engineer to design, build and operate Python/FastAPI services that extract entities and relationships from unstructured documents, resolve them to canonical identifiers, and support graph-augmented retrieval.

You will work with Neo4j/Amazon Neptune, Cypher, Docker, and AWS to scale to millions of nodes and edges, implement ontology, and enable production-grade graph and vector retrieval systems.

Qualifications

• 5–9 years software engineering with knowledge-graph and NLP production experience.
• Experience building knowledge graphs from unstructured text, not just CRUD on graphs.
• Production-scale graph systems with millions of nodes/edges and bounded traversal depth.
• Entity resolution at scale with efficient blocking and candidate generation.
• Experience with graph databases and graph-augmented vector retrieval.
• Proficiency in Python and FastAPI in production environments.

Responsibilities

• Design, build and operate Python/FastAPI services for entity/relationship extraction from unstructured documents.
• Implement entity resolution to canonical identifiers and maintain the knowledge graph.
• Develop ontology/schema, node/edge deduplication, and provenance tracking.
• Integrate graph results with vector search for multi-hop relational queries.
• Instrument and support services in production and monitor performance.

Skills

Knowledge graph

NLP

Python

FastAPI

Production engineering

Graph databases

Cypher/openCypher

Docker

AWS

Entity resolution

Tools

Neo4j

Amazon Neptune

Cypher/openCypher

Docker

AWS (S3, ECS/EKS)

Job description

Chryselys is a Great Place to Work Certified Pharma Analytics & Business consulting company that delivers data-driven insights leveraging AI-powered, cloud-native platforms to achieve high-impact transformations. We specialize in digital technologies and advanced data science techniques that provide strategic and operational insights.

Role Summary

Design, build and operate the Python/FastAPI services that extract entities and relationships from unstructured documents, resolve them to canonical identifiers, maintain the knowledge graph, and serve graph-augmented retrieval alongside vector search for multi-hop and relational questions.

Responsibilities

• Build entity and relation extraction services over unstructured documents — molecules, brands, indications, therapeutic areas, endpoints, claims.
• Build entity resolution: alias handling, blocking and candidate generation, fuzzy and embedding matching, calibrated thresholds, human review routing.
• Design and maintain the graph schema and ontology; incremental ingest, node and edge deduplication and merging, provenance on every edge.
• Fuse graph and vector results into a single ranked, cited context for the retrieval service.
• Instrument, monitor and support the services in production.

Qualifications

• 5–9 years software engineering, with demonstrable knowledge-graph construction and applied NLP delivered to production.
• Has built a knowledge graph from unstructured text — not queried an existing one, and not a CRUD application on a graph database.
• Graph at production scale. Millions of nodes and edges; incremental updates with stable node identity; supernode and traversal-explosion handling with bounded depth and timeouts.
• Entity resolution at corpus scale. Blocking and candidate generation that avoid O(n²) comparison, with measured precision on a labelled sample.
• Graph database in production. Neo4j, Amazon Neptune or equivalent; Cypher / openCypher fluency.
• Ontology and taxonomy modelling. Schema evolution without breaking downstream consumers; judgement on node vs. edge vs. property.
• Extraction. NER and relation extraction — LLM-based, model-based (spaCy, scispaCy, transformers) or hybrid, with the judgement to choose.
• Graph vs. vector judgement. Knows where graph retrieval wins — multi-hop, relational, comparative and aggregate questions — and that hybrid is the production norm.
• Python and FastAPI in production. Python 3.11+, async, Pydantic, Docker, pytest, Git and CI; AWS as a consumer (S3, ECS/EKS, Bedrock, Neptune or self-hosted Neo4j).

Preferred

• Biomedical ontologies and registries: UMLS, MeSH, SNOMED, RxNorm, ICD-10, DrugBank, ChEMBL.
• Life sciences or pharma domain experience; RDF/SPARQL alongside property graphs.
• GraphRAG approaches: community detection for corpus-level summarisation, local vs. global search.