AI Architect

Roles and Responsibilities:

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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:

• 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:

• 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.
• 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.
• 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.

We welcome talent at all career stages and are dedicated to understanding and supporting additional needs. We're proud to be an equal opportunity employer, committed to creating an inclusive and open environment for everyone.