Associate Principal - Architecture

Job Summary

Seeking a seasoned AIGenAI Architect with expertise in financial services cloud AI solutions and enterprise architecture to lead the design and deployment of scalable productiongrade AI platforms

Job Description

12-16 years of software engineering or architecture experience including 4 years in AI Generative AI or ML solutioning Strong understanding of financial services operating models across banking insurance payments wealth management regulatory reporting fraud risk and customer lifecycle processes Proven experience in building and deploying AI solutions in production environments Handson programming skills in Python and familiarity with ML frameworks such as TensorFlow or PyTorch Extensive experience with cloud platforms including AWS Cloud Architecture Azure AI and GCP Proficient with AI coding assistant tools such as Claude Code GitHub Copilot and Cursor Deep knowledge of data privacy and protection laws including GDPR and the EU AI Act Expertise in modern GenAI patterns including LLMs RAG architectures vector databases agentic workflows prompt engineering evaluation frameworks AI guardrails knowledge grounding and enterprise integration patterns Experience with CICD architecture model lifecycle management observability drift monitoring automated testing security controls and cost optimization Ability to embed responsible AI principles into solution design emphasizing explainability humanintheloop controls auditability regulatory alignment and risk mitigation Strong design thinking and ideation workshop facilitation skills Solid expertise in Enterprise Architecture to align AI solutions with overall business and IT strategies Bachelors or masters degree in Computer Science Data Science or a related field Preferred certifications in AIML and contributions to reusable AI accelerators reference architectures or thought leadership

Roles and Responsibilities

Lead the design and deployment of scalable productiongrade AI platforms incorporating CICD pipelines and observability

Collaborate with business leaders technology executives enterprise architects data teams risk and compliance stakeholders and delivery teams to develop pragmatic AI adoption roadmaps

Drive the application of AIGenerative AI technologies using tools like Claude Code and GitHub Copilot to accelerate development

Implement and oversee responsible AI practices ensuring compliance with data privacy and protection laws

Facilitate design thinking and ideation workshops to innovate AI solutions aligned with business needs

Develop and maintain prompt engineering strategies and leverage RAGAS Retrieval Augmented Generation Assessment frameworks for enhanced AI performance

Monitor AI model performance including drift monitoring and automated testing to ensure robustness and reliability

Advocate for and embed AI guardrails security controls and cost optimization in AI platform design

Mentor and guide engineering teams on best practices in AIML solutioning cloud architecture and enterprise architecture