Senior GenAI Engineer

Global Business Ser. 4u; New York (NY); On-site; USD 150,000 - 220,000; Full time

Job summary

Global Business Ser. 4u seeks a Senior GenAI / RAG Engineer to architect and implement enterprise-grade AI solutions for banking and financial services.

You will design end-to-end RAG pipelines, perform document ingestion and embedding workflows, and integrate with AML rule engines and case-management systems. Responsibilities include building based on Azure OpenAI, Pinecone or equivalent vector databases, and scalable Python microservices with REST APIs.

Qualifications

• Experience building production-grade AI solutions in Banking/Financial Services.
• Ability to design end-to-end RAG architectures and integrate with AML rule engines.
• Proficiency with Azure OpenAI/LLM services and vector databases.
• Experience with document ingestion pipelines, embeddings, and vector search.
• Strong Python-based microservices and REST API development skills.

Responsibilities

• Design and develop enterprise-grade RAG and Generative AI solutions for banking use cases.
• Build RAG pipelines covering ingestion, embeddings, vector search, retrieval, and prompt construction.
• Integrate RAG with AML rule engines and investigation/case-management systems.
• Ensure grounded responses with source citations and mitigated hallucinations.
• Work with Pinecone or similar vector databases and Azure storage/services.
• Develop scalable Python microservices and REST APIs; handle Kafka topics and fault tolerance.
• Collaborate with architects, data engineers, product teams, and AML experts.

Skills

GenAI

RAG

Azure

vector databases

event-driven microservices

AML/KYC

document ingestion

embeddings

vector search

prompt construction

LLM response generation

Pinecone

Azure Blob Storage

Azure OpenAI

Python

REST APIs

Kafka

metadata filtering

similarity thresholds

document versioning

relevance optimization

Tools

Pinecone

Azure Blob Storage

Azure OpenAI

Kafka

Job description

We are seeking a Senior GenAI / RAG Engineer with strong experience in Generative AI, Retrieval-Augmented Generation (RAG), Azure, vector databases, and event-driven microservices experience building production-grade AI solutions in the Banking/Financial Services domain, preferably with AML/KYC use cases.

The candidate should be able to design, develop, and explain an end-to-end RAG architecture and understand how GenAI solutions integrate with existing deterministic systems such as AML rule engines.

Key Responsibilities

• Design and develop enterprise-grade RAG and Generative AI solutions for banking and financial services use cases.
• Build RAG pipelines covering document ingestion, chunking, embeddings, vector search, retrieval, reranking, prompt construction, and LLM response generation.
• Integrate RAG solutions with existing AML rule engines and investigation/case-management systems.
• Ensure RAG complements deterministic AML decisioning rather than duplicating or re-running existing business rules.
• Implement grounded responses with source citations, evidence retrieval, and hallucination mitigation.
• Work with Pinecone or similar vector databases for semantic search and retrieval.
• Use Azure Blob Storage for document/file storage and build document ingestion pipelines.
• Integrate Azure OpenAI/LLM services into enterprise applications.
• Design and develop scalable Python-based microservices and REST APIs.
• Work with Kafka topics, partitions, consumer groups, replication, ISR, offsets, retries, and fault-tolerant processing.
• Implement metadata filtering, similarity thresholds, document versioning, and relevance optimization within RAG pipelines.
• Collaborate with architects, developers, data engineers, product teams, and banking/AML subject matter experts.