GenAI Architect - Banking

Unison Group; Singapore; In loco; SGD 180.000 - 300.000; Tempo pieno

Descrizione del lavoro

Unison Group seeks a GenAI Architect to design and implement secure, scalable Generative AI solutions for banking, spanning customer service, operations, risk, and compliance. The role blends enterprise architecture with hands‑on AI expertise and banking domain knowledge.

You will lead architecture reviews, mentor engineers, and guide projects from PoC to production, ensuring regulatory alignment and auditable workflows across core systems and APIs.

Competenze

• Bachelor’s or master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Strong experience in enterprise architecture and application delivery within banking or financial services.
• Hands‑on experience developing and deploying production LLM applications.
• Proficiency in Python, API development, microservices, and distributed systems.
• Strong understanding of RAG, embeddings, semantic search, reranking, prompt engineering, and model evaluation.
• Experience with at least one major cloud AI platform, such as Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
• Experience with vector databases or vector‑enabled search platforms.
• Knowledge of banking processes such as retail or corporate banking, lending, payments, KYC/AML, or risk management.
• Understanding of secure development, encryption, identity and access management, and audit logging.
• Experience with CI/CD, containers, infrastructure as code, and LLMOps.
• Strong communication skills and the ability to explain architectural decisions to technical and business stakeholders.

Mansioni

• Define the GenAI architecture and roadmap in collaboration with banking business and technology teams.
• Design solutions for use cases such as customer and employee assistants, credit document analysis, KYC support, regulatory knowledge retrieval, and operations automation.
• Build retrieval‑augmented generation (RAG) architectures using banking policies, product documents, procedures, and approved knowledge sources.
• Evaluate LLMs, embedding models, vector databases, and orchestration frameworks against accuracy, security, latency, and cost requirements.
• Integrate GenAI applications with core banking systems, CRM platforms, document repositories, and enterprise APIs.
• Design agentic workflows with controlled tool access, human approvals, and auditable actions.
• Implement controls for sensitive customer data, access permissions, data retention, and data residency.
• Address risks including hallucinations, prompt injection, data leakage, and inappropriate model outputs.
• Establish evaluation, monitoring, versioning, and deployment practices for production AI applications.
• Collaborate with information security, compliance, legal, and model risk teams to meet applicable regulatory requirements and internal policies.
• Lead architecture reviews, mentor engineers, and guide solutions from proof of concept to production.

Conoscenze

GenAI architecture

Enterprise architecture

Python

API development

LLM deployment

Cloud platforms

Vector databases

KYC/AML knowledge

Security controls

CI/CD / LLMOps

Formazione

Bachelor's or Master’s in CS/Engineering or related field

Strumenti

Azure OpenAI

Amazon Bedrock

Google Vertex AI

Descrizione del lavoro

Job Description: GenAI Architect – Banking

Experience: 10+ years in software engineering or architecture, with hands‑on experience delivering Generative AI solutions
Domain: Banking and Financial Services

Role Overview

We are seeking a GenAI Architect to design and implement secure, scalable Generative AI solutions for banking. The role combines enterprise architecture, hands‑on AI expertise, and banking domain knowledge to deliver solutions across customer service, operations, risk, and compliance.

Key Responsibilities

• Define the GenAI architecture and roadmap in collaboration with banking business and technology teams.
• Design solutions for use cases such as customer and employee assistants, credit document analysis, KYC support, regulatory knowledge retrieval, and operations automation.
• Build retrieval‑augmented generation (RAG) architectures using banking policies, product documents, procedures, and approved knowledge sources.
• Evaluate LLMs, embedding models, vector databases, and orchestration frameworks against accuracy, security, latency, and cost requirements.
• Integrate GenAI applications with core banking systems, CRM platforms, document repositories, and enterprise APIs.
• Design agentic workflows with controlled tool access, human approvals, and auditable actions.
• Implement controls for sensitive customer data, access permissions, data retention, and data residency.
• Address risks including hallucinations, prompt injection, data leakage, and inappropriate model outputs.
• Establish evaluation, monitoring, versioning, and deployment practices for production AI applications.
• Collaborate with information security, compliance, legal, and model risk teams to meet applicable regulatory requirements and internal policies.
• Lead architecture reviews, mentor engineers, and guide solutions from proof of concept to production.

Required Skills and Qualifications

• Bachelor’s or master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Strong experience in enterprise architecture and application delivery within banking or financial services.
• Hands‑on experience developing and deploying production LLM applications.
• Proficiency in Python, API development, microservices, and distributed systems.
• Strong understanding of RAG, embeddings, semantic search, reranking, prompt engineering, and model evaluation.
• Experience with at least one major cloud AI platform, such as Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
• Experience with vector databases or vector‑enabled search platforms.
• Knowledge of banking processes such as retail or corporate banking, lending, payments, KYC/AML, or risk management.
• Understanding of secure development, encryption, identity and access management, and audit logging.
• Experience with CI/CD, containers, infrastructure as code, and LLMOps.
• Strong communication skills and the ability to explain architectural decisions to technical and business stakeholders.

Preferred Qualifications

• Experience navigating model risk reviews and AI governance processes in a bank.
• Familiarity with private networking, hybrid deployments, and restricted data environments.
• Experience with model fine‑tuning, open- weight models, or multimodal document processing.
• Experience designing human review and escalation mechanisms for sensitive banking workflows.
• Relevant cloud architecture or AI certifications.