Graph RAG - Knowledge Engineer (Shift 6 PM To 3 AM)

Graph RAG - Knowledge Engineer

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(n) 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.