Knowledge Graph Engineer

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.

If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role: Knowledge Graph Engineer
Experience Level: 5 to 7 Years
Work location: Mumbai/Bangalore/Trivandrum

RESPONSIBILITIES

. Implement graph schemas including entity types, relationship types, property definitions, and provenance attributes against client-provided specifications.
. Design and implement concept linking logic connecting entities across graph boundaries with full provenance tracking.
. Manage schema versioning and schema evolution without requiring full re-ingestion.
. Build idempotent batch ETL pipelines ingesting structured data from FHIR APIs, ontology datasets, and metadata sources into a graph database at scale.
. Write and optimise graph queries (Cypher, SPARQL, or Gremlin) for production access patterns: entity lookup, multi-hop traversal, hierarchy navigation, and lineage queries.
. Tune query performance to meet latency and concurrency targets as the graph scales to tens of millions of records.
. Expose graph access patterns as MCP tools for consumption by LLM-based orchestration agents.
. Define and execute retrieval quality benchmarks: precision, recall, traversal accuracy, latency against test query sets and produce validation reports.
. Author operational runbooks, schema versioning procedures, and handoff documentation

MUST-HAVE REQUIREMENTS
Minimum 5 years overall experience with at least 3 years in production knowledge graph or graph database engineering.

Knowledge Graph Engineering:

Production-grade experience building knowledge graphs: entity modelling,
relationship typing, provenance attributes, and schema versioning in a live
operational system with concurrent users and continuous data updates.

Graph Database Platforms

Hands-on experience deploying and operating at least one graph DB
platform in production: Neo4j, Amazon Neptune, TigerGraph, Google
Spanner Graph, Cosmos DB (Gremlin API), Stardog, or equivalent. Able to
configure, tune, and troubleshoot under load.

Graph Query Languages

Proficient in at least one of Cypher, SPARQL, or Gremlin. Able to write and
optimise queries for production latency targets and design access patterns
for multi-hop traversals at scale.

Ingestion Pipelines

Able to build production batch and streaming data pipelines end-to-end. Experience with at least one streaming framework (Kafka, Pub/Sub,
Kinesis) and one processing or orchestration tool (Airflow, Apache Beam /
Dataflow, Spark).

API Development

Experience designing and building versioned REST APIs for graph data
exposure with typed schemas, access control, audit logging, and API
gateway integration.

Cloud Platforms

Hands-on with GCP or Azure: IAM, secrets management, managed service
deployment, networking, and Terraform for infrastructure-as-code.

Regulated Data Environments

Experience with sensitive or regulated data (PHI, PII, or equivalent) where
access control, audit logging, and compliance reviews are standard
practice. Healthcare, life sciences, pharma, or financial services
backgrounds are all relevant.

Programming

Production-grade Python for pipeline development and data transformation.

Maintainable, testable code with proper error handling.