AI Architect

JOB DESCRIPTION

Location	India
Reports To
Level	Individual Contributor / Architect (8+ years experience)
Team	Enterprise Data Services (EDS)
Contract	Permanent, Full-Time
Line Relationship	Data Architect

About the Team

Enterprise Data Services (EDS) is responsible for Bottomline's Enterprise Data Warehouse, Enterprise Analytics, Artificial Intelligence, Data Governance, and strategic data platforms.

Our mission is to transform business operations, products, and decision-making through trusted data and AI-powered capabilities. Our technology strategy is centred around Snowflake and its native AI ecosystem, including Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, Snowpark, and enterprise data products such as Customer Cube.

We operate at the intersection of Data Engineering, AI Engineering, Analytics, Governance, and Platform Engineering, building the foundations that enable the organisation to become truly AI-first.

The Role

We are seeking an experienced Enterprise AI & Data Architect to define and lead the future-state architecture for Bottomline's Data, Analytics, AI, and Cloud Platforms.

This role combines AI Architecture, Enterprise Data Architecture, Data Engineering Architecture, and Cloud Platform Architecture to ensure the organisation can scale trusted data products, AI-native solutions, and business-critical analytics on a secure, governed, resilient, and cost-effective foundation.

The Enterprise AI & Data Architect will report directly to the Senior Director of AI & Data Services and work closely with the existing Data Architect through a dotted-line relationship. Together, these roles will define and govern the enterprise architecture strategy for data, analytics, AI, integration, and cloud platforms.

While the Data Architect focuses on information architecture, data modelling, governance, and enterprise data standards, the Enterprise AI & Data Architect will focus on AI architecture, platform architecture, data engineering standards, cloud infrastructure, and the technical enablement of enterprise-scale AI solutions.

This role is both strategic and hands-on, requiring the ability to define long-term architecture roadmaps whilst also guiding engineering teams through the delivery of complex initiatives.

What You Will Do

Enterprise AI Strategy & Architecture

• Support the enterprise AI architecture roadmap.

• Develop reference architectures for Generative AI, Agentic AI, Predictive AI, and AI-powered analytics.

• Establish standards and design patterns for Cortex AI, Cortex Agents, Cortex Search, and Cortex Analyst.

• Define architecture principles for AI platforms, reusable AI services, and enterprise AI products.

• Evaluate emerging AI technologies and determine their applicability to Bottomline.

• Create architectural blueprints that enable scalable and governed AI adoption across the organisation.

Data Platform & Data Engineering Architecture

• Define architecture standards for the Enterprise Data Warehouse and strategic data products.

• Design scalable, AI-ready data architectures that support analytics, machine learning, and enterprise reporting.

• Establish patterns for batch, streaming, CDC, event-driven, and real-time data integration.

• Define standards for data ingestion, transformation, orchestration, testing, deployment, and operational support.

• Partner with engineering teams to ensure solutions are performant, scalable, maintainable, and cost-efficient.

• Guide architecture for Customer Cube and other strategic data products.

• Drive adoption of modern DataOps, DevOps, and platform engineering practices.

Cloud Infrastructure & Platform Architecture

• Define cloud architecture patterns supporting Snowflake, AI workloads, data pipelines, and analytics services.

• Establish standards for CI/CD, Infrastructure as Code (IaC), platform automation, and deployment frameworks.

• Design scalable and resilient architectures capable of supporting enterprise AI workloads.

• Define standards for observability, monitoring, logging, alerting, and platform health management.

• Support infrastructure modernisation and platform optimisation initiatives.

• Partner with Infrastructure and Security teams to ensure enterprise-grade operational practices.

Resilience, Disaster Recovery & Operational Readiness

• Define architectural standards for disaster recovery, backup, resilience, and business continuity.

• Ensure critical data and AI platforms have documented and tested recovery capabilities.

• Guide architecture decisions related to high availability and system resilience.

• Support audit, risk, compliance, and security initiatives through robust architectural controls.

• Define operational readiness requirements for production AI and data services.

AI Governance & Responsible AI

• Establish enterprise AI governance frameworks.

• Define standards for model evaluation, monitoring, explainability, fairness, and risk management.

• Create architectural guardrails for AI development and deployment.

• Collaborate with Legal, Compliance, Security, and Governance teams to ensure regulatory alignment.

• Drive adoption of responsible AI principles across all AI initiatives.

Architecture Leadership & Collaboration

• Partner closely with the Data Architect to develop a unified enterprise data and AI architecture strategy.

• Participate in architecture governance forums and design reviews.

• Define and maintain architecture standards across Data Engineering, AI Engineering, Analytics, and Platform Engineering.

• Mentor engineers and architects on best practices and emerging technologies.

• Review and approve solution architectures for major strategic initiatives.

• Act as a trusted advisor to business and technology leadership.

Business & Stakeholder Engagement

• Translate business strategy into executable technology roadmaps.

• Communicate complex technical concepts to both technical and non-technical audiences.

• Support organisational AI adoption and transformation initiatives.

Requirements

Must Have

• 8+ years of experience in Enterprise Architecture, Data Architecture, AI Architecture, Cloud Architecture, or related disciplines.

• Strong experience designing large-scale enterprise data platforms and modern cloud architectures.

• Deep understanding of Data Engineering, Analytics, Artificial Intelligence, and Machine Learning ecosystems.

• Strong knowledge of SQL and Python.

• Experience designing scalable, secure, and resilient enterprise systems.

• Experience defining architecture standards, governance processes, and technology roadmaps.

• Strong stakeholder management and communication skills.

• Proven experience leading cross-functional technology initiatives.

Strong Preference

• Deep experience with Snowflake and associated platform services.

• Experience with Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, and Snowpark.

• Experience with Neo4J/native graph databases.

• Experience building AI-ready enterprise data platforms.

• Knowledge of Generative AI architectures, including Retrieval-Augmented Generation (RAG), Agentic AI, vector search, semantic layers, evaluation frameworks, and LLMOps.

• Experience with AWS cloud architecture and platform engineering.

• Experience implementing Infrastructure as Code and automated deployment frameworks.

• Strong understanding of Data Governance, Data Quality, Metadata Management, and Information Security.

Nice to Have

• Fintech, Payments, Banking, or B2B SaaS experience.

• Snowflake certification(s).

• Cloud Architecture certification(s).

• Experience presenting architecture strategy to Executive Leadership Teams.

• Experience supporting enterprise AI transformation programmes.

• Knowledge of Data Mesh, Lakehouse, or modern distributed data architecture patterns.

Our Technology Stack

Data & AI Platform	Snowflake; Cortex AI; Cortex Agents; Cortex Search; Cortex Analyst; Snowpark; Dynamic Tables; Enterprise Data Warehouse
Cloud & Infrastructure	AWS; Infrastructure as Code; CI/CD Pipelines; Platform Automation; Monitoring & Observability Tooling
Engineering	Python; SQL; Git; DataOps; DevOps
Analytics	Power BI; Streamlit; Enterprise Semantic Models

Why This Role Is Different

• Shape the future of Bottomline's enterprise AI and data ecosystem.

• Own architecture across AI, Data, Analytics, Cloud, and Platform Engineering.

• Influence strategic technology and investment decisions.

• Build reusable enterprise capabilities rather than one-off solutions.

• Operate at the intersection of business strategy and technical execution.

• Work directly with senior leadership on high-impact initiatives.

• Drive the adoption of trusted, governed, and scalable AI solutions across the organisation.

Your First 90 Days

Days 1-30: Understand

• Review the current data, AI, analytics, and platform architecture landscape.

• Meet key stakeholders across Data Engineering, AI Engineering, Infrastructure, Security, Product, and Business teams.

• Assess strategic programmes including Customer Cube and enterprise AI initiatives.

• Identify architecture opportunities, gaps, and risks.

Days 30-60: Define

• Develop the enterprise AI and data architecture roadmap.

• Publish initial architecture principles, standards, and governance processes.

• Define target-state architectures for strategic AI and data platforms.

• Establish architecture review and approval processes.

Days 60-90: Enable

• Launch architecture governance forums.

• Support delivery teams with reference architectures and reusable patterns.

• Define platform engineering, infrastructure, and observability standards.

• Deliver initial architecture blueprints for strategic programmes.

• Present the enterprise AI and data architecture roadmap to senior leadership.

Success Measures

After 12 months, success would include:

• Enterprise AI and Data Architecture adopted across strategic initiatives.

• Standardised architecture and engineering standards implemented across Data and AI teams.

• Customer Cube established as a strategic enterprise data product.

• Increased delivery velocity through reusable architecture patterns and shared services.

• Reduced technical debt and improved platform maintainability.

• Improved Snowflake performance, scalability, reliability, and cost efficiency.

• Fully governed AI development lifecycle with measurable quality controls.

• Mature disaster recovery and resilience capabilities across critical platforms.

• Strong collaboration between Data Architecture, Data Engineering, AI Engineering, Infrastructure, and Security teams.

• Demonstrable business outcomes delivered through scalable, trusted, and AI-enabled solutions.