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.