Salary :
Primary Skills
AI Architect – Data & AI Platforms (Banking)
12-15 years of experience in Data Architecture, Data Engineering, AI Data Platforms, and AI/GenAI solutions, with proven experience designing and delivering the large scale projectsKey Tags - AWS, Snowflake Cortex, Banking domain, Hands on experience
Specialization
- Data Science Foundation: Senior Data Scientist/ AI Architect
Job requirements
AI Architect – Data & AI Platforms (Banking)
12-15 years of experience in Data Architecture, Data Engineering, AI Data Platforms, and AI/GenAI solutions, with proven experience designing and delivering the large scale projectsKey Tags - AWS, Snowflake, Banking domain, Hands on experience
Role Overview
We are seeking an experienced AI Architect with strong Banking and Financial Services domain expertise to lead the design and implementation of enterprise Data & AI platforms. The ideal candidate will possess deep expertise in Snowflake and AWS, strong data architecture and engineering fundamentals, and hands-on experience architecting Generative AI and Agentic AI solutions.
Key Responsibilities
• Lead the architecture and design of enterprise Data & AI platforms supporting analytics, reporting, and AI-driven business capabilities.
• Partner with business and technology stakeholders to identify and prioritize high-value AI and data modernization opportunities.
• Define scalable data architecture, governance, security, and compliance frameworks aligned with banking and regulatory requirements.
• Architect and guide implementation of AI, Generative AI, and Agentic AI solutions leveraging enterprise data assets.
• Provide technical leadership across data engineering, AI engineering, and platform teams.
• Establish architecture standards, best practices, and future-state roadmaps for data and AI capabilities.
Required Skills & Experience
• Strong Banking and Financial Services domain experience with understanding of lending, commercial banking, risk, finance, regulatory reporting, customer data, and enterprise data management.
• Proven experience designing and implementing enterprise-scale data platforms in highly regulated environments with a strong focus on governance, security, compliance, and auditability.
• Deep expertise in data architecture, data engineering, data warehousing, data lakes, lakehouse architectures, data modeling, metadata management, and data governance.
• Hands-on experience designing end-to-end data platforms, including ingestion, integration, transformation, storage, data quality, lineage, and consumption layers.
• Strong expertise with Snowflake, including Snowpark, Cortex, Cortex Analyst, Cortex Search, Iceberg integration, data sharing, security, and performance optimization.
• Strong experience with AWS services including S3, Glue, Lambda, EMR, ECS/EKS, and related cloud-native data engineering services.
• Experience architecting modern cloud-based data platforms leveraging Snowflake and AWS.
• Strong understanding of Generative AI, LLMs, RAG, Vector Databases, AI Agents, Multi-Agent Architectures, and Agentic AI patterns.
• Hands-on experience designing and deploying AI solutions using Snowflake Cortex, AWS Bedrock, and enterprise AI frameworks.
• Experience integrating AI capabilities into enterprise data platforms while ensuring governance, security, and responsible AI practices.
• Excellent stakeholder management, communication, and leadership skills with the ability to bridge business and technology teams.
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