Sr. Data and GenAI Engineer (Finance Domain)

Dallas, IA - USA

Monthly Salary: Not Disclosed

Posted on: 3 hours ago

Vacancies: 1 Vacancy

Job Summary

Role: Sr. Data and GenAI Engineer (Finance Domain).

Location: Dallas TX (Onsite).

Duration: Long Term Contract.

Reworked Job Summary:

The Sr Data and GenAI Engineer will architect knowledge graph and vector database infrastructure optimized for LLM fine-tuning and low-latency inference in financial services. This hands-on role owns GraphDB/Neo4j pipeline design RAG systems embedding generation and data preprocessing for enterprise-grade AI serving financial domain use cases (transactions risk compliance).

Key Responsibilities:

Graph & Vector Infrastructure:

Design GraphDB solutions (Neo4j Amazon Neptune) modeling complex financial relationships (counterparties transactions risk exposures ownership hierarchies).
Implement VectorDB infrastructure (Pinecone Weaviate pgvector) for semantic search and RAG supporting financial document retrieval.
Build hybrid GraphRAG pipelines combining structured relationship traversal with unstructured semantic similarity.

Knowledge Graph Pipelines:

Create multi-source knowledge graph ingestion from transactional systems market data KYC/AML feeds and regulatory documents.
Implement entity resolution relationship extraction and temporal graph modeling for financial lineage and compliance.
Design graph query optimization for real-time risk analysis and recommendation systems.

LLM Data Preparation:

Build scalable embedding pipelines using Sentence Transformers OpenAI embeddings or financial domain models.
Implement data preprocessing workflows for LLM fine-tuning (deduplication chunking metadata enrichment).
Orchestrate RAG pipelines with financial context retrieval prompt engineering and response synthesis.

Model Deployment & Inference:

Deploy fine-tuned LLMs on GPU servers (NVIDIA A100/H100) with vLLM TensorRT-LLM or TGI for optimized inference.
Implement model serving infrastructure with auto-scaling request queuing and latency monitoring.
Design multi-tenant isolation and cost governance for production AI workloads.

Data Governance & Compliance:

Ensure data lineage auditability and regulatory compliance (SEC FINRA GDPR) across AI pipelines.
Implement access controls PII masking and model explainability for financial governance.

Technical Stack:

Graph Database:

Neo4j: Cypher queries APOC Bloom visualization.
Amazon Neptune: Gremlin/SPARQL GraphRAG.
TigerGraph: GSQL for financial analytics.

Vector Database:

Pinecone: Serverless metadata filtering.
Weaviate: Hybrid search GraphQL.
pgvector: PostgreSQL native vectors.
Milvus: High-throughput financial embeddings.

LLM Infrastructure:

vLLM/TGI: OpenAI-compatible serving.
TensorRT-LLM: NVIDIA inference optimization.
Ray Serve: Multi-model orchestration.
Kubernetes: GPU auto-scaling.

Data Engineering:

Apache Airflow: Pipeline orchestration.
dbt: Transformation testing.
Great Expectations: Data quality.
Monte Carlo: Observability.

Financial Domain Expertise (Required):

Transaction Graphs: Payment networks trade settlement.
Risk Networks: Counterparty exposure concentration risk.
KYC/AML: Entity resolution sanctions screening.
Compliance: Regulatory relationship mapping.
Wealth Management: Portfolio holdings ownership chains.

Role: Sr. Data and GenAI Engineer (Finance Domain). Location: Dallas TX (Onsite). Duration: Long Term Contract. Reworked Job Summary: The Sr Data and GenAI Engineer will architect knowledge graph and vector database infrastructure optimized for LLM fine-tuning and low-latency inference in financ...