Senior / Lead Solution Architect – Enterprise Applications, Data & AI
Location: Singapore
Employment Type: Full-Time / Contract
Seniority: Senior / Lead
Domain: Enterprise Technology / Financial Services
About the Role
We are seeking an accomplished Senior / Lead Solution Architect to provide end-to-end architecture leadership across complex enterprise technology platforms and transformation initiatives.
The successful candidate will have a strong foundation in enterprise Java-based application architecture, combined with extensive experience across cloud, integration, modern data platforms and emerging AI technologies.
This is not a pure Data Architect, Databricks Engineer or AI Engineer position. We are looking for an experienced enterprise-level Solution Architect who understands how large, mission-critical applications are designed, integrated, modernized and governed, while also being capable of incorporating modern data and AI capabilities into the overall enterprise architecture.
The architect will work closely with enterprise architecture, engineering, infrastructure, data, security, AI and business teams to define scalable, secure, resilient and future-ready solutions.
Key Responsibilities
Solution & Enterprise Architecture
- Own the end-to-end solution architecture for complex enterprise applications and transformation programs.
- Translate business and technology requirements into scalable and sustainable architecture solutions.
- Define current-state, transition-state and target-state architectures.
- Develop High-Level Designs (HLD), solution architecture documents, architecture diagrams, technology roadmaps and integration designs.
- Define and promote reusable architecture patterns, principles and technology standards.
- Ensure proposed solutions align with broader enterprise architecture strategies and technology roadmaps.
- Lead architecture reviews and present solution designs to Architecture Review Boards and senior technology stakeholders.
- Conduct technology assessments and provide recommendations on platforms, frameworks and emerging technologies.
- Provide architecture leadership throughout the complete solution lifecycle from initial design through implementation and production.
Enterprise Application Architecture
- Architect large-scale, business-critical applications based on Java/J2EE and modern Java technologies.
- Provide architectural direction across:
- Java / J2EE
- Spring / Spring Boot
- Microservices
- RESTful APIs
- Distributed systems
- Event-driven architectures
- Enterprise integration
- Messaging platforms
- Define modernization strategies for legacy enterprise applications.
- Drive transformation from monolithic architectures toward microservices, API-driven and cloud-native architectures where appropriate.
- Ensure application architecture meets enterprise requirements for availability, scalability, resilience, maintainability and performance.
Integration & API Architecture
- Define integration architecture across applications, data platforms, cloud services and external systems.
- Design scalable API-led and event-driven architectures.
- Establish patterns for synchronous and asynchronous system integration.
- Provide architecture guidance across:
- API gateways and API management
- REST APIs
- Kafka / event streaming
- Messaging platforms
- Enterprise integration patterns
- Batch and real-time integration
- Ensure appropriate security, authentication, authorization and governance across integration layers.
Cloud Architecture
- Design and govern enterprise solutions deployed across Azure and/or AWS.
- Define cloud-native architecture patterns covering:
- Containers and Kubernetes
- Serverless technologies
- API management
- Event streaming
- Cloud storage
- Identity and Access Management
- Observability and monitoring
- High availability and disaster recovery
- Support enterprise application modernization and cloud migration programs.
- Work with engineering and DevOps teams to establish CI/CD, DevSecOps and Infrastructure-as-Code practices.
Data & Databricks Architecture
The architect should have a strong understanding of modern enterprise data platforms and how they integrate with transactional and operational applications.
Experience should include exposure to:
- Databricks
- Data Lake / Lakehouse architecture
- Delta Lake
- Unity Catalog
- Data ingestion and transformation pipelines
- Batch and streaming data processing
- Structured and unstructured data
- Enterprise data integration
- Data governance and lineage
- Real-time / event-driven data architecture
- Cloud-based data platforms
The candidate is not expected to be a hands-on Databricks Data Engineer. However, they should be capable of designing enterprise solutions where Databricks and modern data platforms form part of the overall architecture.
Artificial Intelligence & Generative AI
The successful candidate should have good architectural understanding and preferably practical experience with enterprise AI and Generative AI solutions.
Relevant areas include:
- Generative AI
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Agentic AI / AI Agents
- AI orchestration
- Vector databases and semantic search
- Enterprise knowledge integration
- Prompt and context management
- AI APIs and model integration
- Azure OpenAI or equivalent enterprise AI platforms
- Machine Learning / MLOps concepts
- AI evaluation and observability
The architect should understand how AI capabilities can be securely integrated into existing enterprise applications, APIs, workflows and data platforms.
Particular importance will be placed on the ability to move AI initiatives from proof-of-concept into secure, scalable and governed production environments.
AI Governance & Security
- Define appropriate architecture controls for enterprise AI solutions.
- Ensure protection of confidential, customer and enterprise data when interacting with AI models.
- Understand key considerations around:
- Responsible AI
- AI security
- Data privacy
- Model governance
- Access control
- Auditability
- Data leakage prevention
- Human-in-the-loop controls
- Work closely with cybersecurity, risk and governance teams to ensure AI solutions meet enterprise standards.
Enterprise Architecture & TOGAF
- Apply enterprise architecture methodologies and principles to solution design.
- Ensure alignment between business, application, data and technology architectures.
- Define architecture principles, standards, reference architectures and roadmaps.
- Participate in enterprise architecture governance and design authority processes.
- Provide architecture guidance across multiple technology domains.
TOGAF certification is strongly preferred.
Required Experience
- 12+ years of overall technology experience, ideally 15+ years for Lead-level candidates.
- Minimum 5+ years in Solution Architecture / Application Architecture / Enterprise Architecture roles.
- Strong previous hands-on engineering background with Java/J2EE enterprise applications.
- Deep understanding of Spring Boot, microservices and distributed application architectures.
- Proven experience designing large-scale, high-availability enterprise applications.
- Strong experience with API and integration architecture.
- Good knowledge of Kafka / event-driven architecture / enterprise messaging.
- Strong architecture experience with Azure and/or AWS.
- Experience with application modernization and cloud transformation.
- Good architectural understanding of Databricks and modern data platforms.
- Understanding of Generative AI and enterprise AI architecture.
- Strong understanding of security, resilience, performance, scalability and technology governance.
- Experience creating HLDs, architecture roadmaps, solution designs and technical standards.
- Strong stakeholder-management and communication capabilities.
- Ability to influence senior business, engineering and technology stakeholders.
Preferred Industry Experience
Candidates with experience in complex, regulated enterprise environments will be highly regarded, particularly within:
- Banking
- Insurance
- Wealth Management
- Financial Services
Knowledge of regulatory, security, privacy and technology-risk requirements within financial services would be advantageous.
Preferred Certifications
The following certifications would be advantageous:
- TOGAF Certified – strongly preferred
- AWS Certified Solutions Architect
- Microsoft Azure Solutions Architect
- Databricks certification
- Kubernetes / Cloud Native certification
- AI / Machine Learning / Generative AI certifications
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