Knowledge Architect – Knowledge Graphs & AI Semantics

Headfield Solutions Pvt Ltd.; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Headfield Solutions Pvt Ltd. in New York is seeking a Lead Knowledge Architect to unify disparate vocabularies across front office, risk, finance, operations and compliance using ontologies and knowledge graphs. You will design a semantic foundation, enable explainable retrieval, and tune AI systems for consistency and trust.

The role requires strong data-architecture experience, hands-on graph tech, and deep investment-domain fluency to align portfolio managers, risk officers and controllers.

Qualifications

• 8+ years in data architecture with 4+ years on production knowledge graphs.
• Hands-on graph modeling: property-graph and RDF/OWL, SHACL, SPARQL, Cypher, Gremlin.
• Strong investment/financial domain knowledge across asset classes and lifecycles.
• Proven experience improving LLM output via semantic grounding (RAG/GraphRAG).
• Experience with data catalogs (Collibra, Alation, Atlan) and MDM; Python and SQL.

Skills

Data architecture

Graph databases

Investment domain knowledge

Communication

Tools

SHACL

SPARQL

Cypher

Gremlin

Collibra

Alation

Atlan

Python

SQL

Spark

Databricks

Snowflake

Job description

We are hiring a Lead Knowledge Architect for an investment and financial-services client whose departments (front office, risk, finance, operations, compliance) describe the same concepts in different vocabularies, causing their AI systems to return inconsistent and uncertain answers. You will build the unified semantic foundation (ontologies, taxonomies, knowledge graphs) that reconciles these vocabularies and tune how AI retrieves and reasons over it so answers become consistent, explainable, and trustworthy. This requires a seasoned data architect with deep graph expertise and enough investment-business fluency to settle semantic disputes between a portfolio manager, a risk officer, and a controller.

What we are looking for:

• 8+ years in data architecture, including 4+ years designing and operating production knowledge graphs or semantic platforms.
• Deep hands-on graph expertise: property-graph and RDF/OWL modeling, SHACL, SPARQL, Cypher, Gremlin, performance tuning, and at least two major graph databases at scale.
• Strong investment and financial domain knowledge: asset classes and instrument lifecycles, portfolio and position data, risk and performance measurement, fund accounting, trade lifecycle, and regulatory context (MiFID II, BCBS 239, SEC/ESMA reporting). Prior work at an asset manager, bank, hedge fund, or financial data vendor strongly preferred.
• Proven experience improving LLM output quality via semantic grounding: RAG/GraphRAG, embeddings and vector search, entity resolution, retrieval evaluation, and hallucination measurement.
• Experience with data catalogs and glossary tooling (Collibra, Alation, Atlan) and master/reference data management; proficiency in Python and SQL and modern data platforms (Spark, Databricks, Snowflake).
• Exceptional facilitation and communication skills; able to align competing stakeholders and translate outcomes into formal models.