Lead Software Engineer – Full Stack, AI & Cloud Systems

2. Role Purpose

We are seeking an experienced Lead Software Engineer – Full Stack, AI & Cloud Systems to join our Singapore-based R&D team. This is a senior hands-on engineering role that combines deep technical execution with architectural leadership.

The successful candidate will drive the design, development, and delivery of intelligent, scalable enterprise software products. This includes AI-powered query systems, cloud-native data pipelines, blockchain-integrated compliance modules, and modern full-stack user interfaces. The role requires proven expertise across AI/ML engineering, cloud platforms (Azure and AWS), distributed ledger technologies(Blockchain/Hyperledger), and contemporary AI-assisted development practices.

Prior professional experience working in Singapore or within the Singapore technology ecosystem is required, given the regulatory, enterprise, and client context in which the role operates.

3. Key Responsibilities

3.1 Architecture & Technical Leadership

•     Own end-to-end system architecture for distributed, cloud-native enterprise products: from infrastructure design through to API contracts, data models, and user-facing application layers.

•     Lead architectural decisions on service decomposition, event-driven system design, multi-tenant data isolation, fault tolerance, and horizontal scalability.

•     Evaluate and select appropriate technology choices across AI, cloud, blockchain, and data stack components, balancing performance, cost, security, and maintainability.

•     Drive technical design documentation, architectural decision records, and engineering standards across the team.

•     Mentor and upskill engineers on the team; establish high standards for code quality, testing practices, and system design patterns.

3.2 AI Systems & Intelligent Application Engineering

•     Design and build AI-powered features and pipelines, including Retrieval-Augmented Generation (RAG) systems, large language model (LLM) integrations, vector search, and semantic query processing.

•     Implement AI-driven natural language interfaces that allow enterprise users to query complex data sets without requiring specialist query languages.

•     Apply advanced prompt engineering techniques to optimise LLM output quality, reduce hallucination, and ensure response accuracy for domain-specific enterprise use cases.

•     Leverage AI-based ‘Vibe Coding ’practices – using AI-assisted code generation, intelligent autocomplete, and automated test creation – to accelerate engineering velocity and improve code quality across the team.

•     Integrate Azure OpenAI, Azure AISearch, Azure Document AI, and equivalent AWS AI/ML services into production applications, managing token budgets, rate limits, and context window constraints.

•     Continuously evaluate emerging AI development tools and practices; champion their adoption where they deliver measurable productivity or quality gains.

3.3 Cloud & Data Platform Engineering

•     Design and implement cloud-native data pipelines on Microsoft Azure and/or AWS, covering event-driven ingestion,transformation, storage, and serving layers.

•     Build and maintain large-scale data processing workflows using Apache Spark/PySpark, Azure Databricks, and Delta Lake, handling enterprise-volume datasets in batch and incremental modes.

•     Configure and optimise cloud-based analytical SQL layers (Azure Synapse Analytics, AWS Redshift, or equivalent)with appropriate security controls including Row-Level Security and data classification.

•     Implement Infrastructure-as-Code deployments (Bicep, Terraform, or CDK) and CI/CD pipelines (GitHub Actions,Azure DevOps) for consistent, repeatable cloud provisioning.

•     Apply cloud security best practices: managed identity, secret management (Azure Key Vault / AWS Secrets Manager), VNet private endpoints, AES-256 encryption at rest, and TLS intransit.

•     Design and enforce data retention, immutability (WORM), and audit logging policies in compliance with relevant Singapore and international regulatory standards.

3.4 Blockchain & Distributed Ledger Engineering

•     Design and implementblockchain-based components for enterprise use cases, including immutable audit trails, decentralised data verification, provenance tracking, and smartcontract-driven workflow automation.

•     Build and deploy permissioned blockchain networks using Hyperledger Fabric, including channel configuration,c haincode (smart contract) development in Go or Node.js, and MSP/identity management.

•     Integrate Hyperledger Fabric networks with enterprise cloud infrastructure (Azure / AWS), ensuring secure peer-to-peer communication, off-chain data linkage, and event-driven triggers from on-chain state changes.

•     Design smart contract logic for compliance, document certification, and data lineage scenarios relevant to regulated financial and enterprise environments.

•     Evaluate and recommend appropriate distributed ledger technology choices for specific use cases: Hyperledger Fabric vs Besu vs public chain integrations, based on throughput, privacy, and regulatory requirements.

•     Stay current with Singapore’s MAS regulatory guidance on digital assets and distributed ledger applications(e.g., MAS FinTech Regulatory Sandbox, Project Guardian) to ensure architecturally sound and compliant implementations.

3.5 Enterprise SAP Systems Integration

•     Design and implement integration architectures that connect enterprise SAP ECC and S/4HANA environments with cloud-native platforms, data pipelines, and AI systems, without introducing dependency on live SAP transactional connectivity in downstream components.

•     Build and configure SAP integration connectors using industry-standard approaches including SAPArchiveLink, SAP Content Server interfaces, RFC/BAPI calls, IDoc processing and SAP BTP (Business Technology Platform) integration flows where applicable.

•     Extract, transform, and load structured data from SAP systems – including SAP FICO, MM, SD, and CO modules –into cloud storage and analytical layers, applying appropriate schema mapping, data type conversion, and audit-grade integrity validation.

•     Understand and work with SAP-proprietary data formats and archive file structures, ensuring schema-agnostic parsing that handles varying segment structures across different SAP configurations without hardcoded field definitions.

•     Implement event-driven or scheduled pipelines that detect, ingest, and validate SAP-sourced data files ast hey arrive in cloud landing zones, triggering downstream processing automatically and routing failed files to quarantine with appropriate alerting.

•     Collaborate with SAP functional consultants (FICO, MM, SD, CO) to understand business data structures, document types, fiscal hierarchies, and master data relationships, translating these into accurate cloud data models and query schemas for AI and analytics components.

•     Apply knowledge of SAP datar etention and audit requirements, including GoBD, GDPdU, SAF-T, and equivalent APAC regulatory standards, to ensure cloud-hosted SAP data extracts meet long-term immutability, accessibility, and audit-readiness obligations.

•     Design SAP integration components for operational resilience: idempotent pipeline runs, safe re-processing of previously ingested files, and clear reconciliation mechanisms between SAP source record counts and cloud-hosted curated data.

3.6 Full-Stack Application Development

•     Build modern, production-grade web applications using React and Next.js for frontend, and Java (Spring Boot),Kotlin, Python, or Node.js for backend services.

•     Design and implement RESTful and event-driven APIs with appropriate authentication (OAuth2, JWT, Entra ID SSO),rate limiting, and observability.

•     Develop workflow automation and integration layers connecting enterprise systems, cloud services, and AI components.

•     Implement responsive, accessible user interfaces optimised for enterprise power users, including data tables, dashboards, and AI-assisted query interfaces.

•     Ensure all application code meets security standards: parameterised queries, input validation, dependency scanning, and OWASP-aligned controls.

3.7 Data & Performance Engineering

•     Design data-intensive processing workflows for large-scale enterprise datasets, applying columnar storage formats (Parquet, Delta), partitioning strategies, and query optimisation techniques.

•     Apply distributed computing frameworks (Apache Spark) for batch aggregation, data quality validation, and transformation at scale.

•     Profile and optimise system performance at all layers: database query plans, API response times, AI pipeline latency, and frontend rendering.

•     Design and implement observability stacks: structured logging, distributed tracing, metrics collection, and alerting using Azure Monitor, Application Insights, or equivalent AWS tooling.

3.8 Stakeholder Collaboration& Delivery

•     Work closely with product owners,business analysts, and domain experts to translate ambiguous enterprise requirements into structured, well-scoped technical solutions.

•     Coordinate with geographically distributed teams across Singapore, Australia, and South-East Asia, managing cross-timezone technical dependencies.

•     Contribute to pre-sales technical engagements, architecture workshops, and client-facing solution demonstrations where required.

•     Participate in agile delivery ceremonies; produce clear estimates, flag risks early, and maintain momentum toward sprint and milestone goals.

4. Qualifications &Experience

4.1 Education

•     Bachelor’s degree (or higher) in Computer Science, Software Engineering, Information Technology, or a closely related discipline.

•     Industry-recognised AI or cloudcertification is strongly preferred, for example: Microsoft Azure AI EngineerAssociate, Azure Solutions Architect Expert, AWS Solutions ArchitectProfessional, or equivalent.

4.2 Technical Experience

•     Minimum 7 years of professionalsoftware engineering experience, with at least 3 years in a senior or leadengineer / architect role.

•     Demonstrated experience designingand delivering production AI/ML systems, including RAG pipelines, LLMintegrations, and vector search platforms.

•     Solid foundation in AI-assisteddevelopment (‘Vibe Coding’): hands-on experience using AI code generation tools(e.g., GitHub Copilot, Cursor, or equivalent), prompt engineering for codetasks, and AI-automated test creation in a professional software engineeringcontext.

•     Hands-on blockchain engineeringexperience with Hyperledger Fabric: chaincode development, channel and MSPconfiguration, and production network deployment. Experience with otherHyperledger projects (Besu, Indy, Aries) is an advantage.

•     Proven experience with MicrosoftAzure cloud services: Azure Blob Storage, Azure Data Factory, Azure Databricks,Azure Synapse Analytics, Azure OpenAI, Azure AI Search, Azure Event Grid, AzureKey Vault, and Microsoft Entra ID.

•     Experience with AWS cloudservices: EC2, S3, Lambda, and associated managed data and AI/ML services.

•     Strong full-stack developmentskills: backend (Java, Kotlin, Python, Node.js, Spring Boot), frontend (React,Next.js), and scripting/automation (Python, PowerShell, Bash, Scala).

•     Experience with enterprise dataplatforms: Apache Spark/PySpark, Delta Lake, Parquet, columnar storage, and SQLanalytical databases (Synapse, Redshift, Oracle, Postgres).

•     Solid understanding of securityengineering: OAuth2, JWT, SSO, RBAC, Row-Level Security, VNet privateendpoints, and secrets management.

•     Experience with Docker, containerorchestration (Kubernetes preferred), CI/CD pipelines, andInfrastructure-as-Code (Bicep or Terraform).

•     Familiarity with distributed datastores: MongoDB, Redis, MySQL; and distributed messaging/event streamingsystems.

•     Demonstrated experienceintegrating with SAP enterprise systems: working knowledge of SAP dataextraction mechanisms, SAP ArchiveLink or equivalent content archivinginterfaces, and SAP-proprietary flat file formats produced by SAP ECC archivingand data retention tools.

4.3 Singapore Experience(Required)

•     Prior professional work experiencein Singapore is required. Candidates must demonstrate familiarity withSingapore’s enterprise technology landscape, regulatory environment, andbusiness culture.

•     Understanding of relevantSingapore regulatory frameworks applicable to enterprise software, financialdata, and technology systems is expected. This includes awareness of MASTechnology Risk Management (TRM) guidelines, PDPA data protection obligations,and Singapore’s broader Smart Nation and digital economy initiatives.

•     Experience collaborating withSingapore-based enterprise clients, government-linked companies (GLCs), orpublic sector organisations in a technology delivery capacity is advantageous.

4.4 Domain Knowledge (Preferred)

•     Background in enterprise SaaS,fintech, compliance technology, or legacy enterprise system modernisationprojects.

•     Exposure to regulated-industrydata management requirements: financial audit trail standards, long-term dataretention obligations, or equivalent compliance-driven data governancecontexts.

•     Experience with smart contracts for compliance, provenance, or document certification use cases in an enterprise setting.

•     Working knowledge of SAP ECC orS/4HANA functional modules – particularly SAP FI (Financial Accounting), CO(Controlling), MM (Materials Management), and SD (Sales & Distribution) –with an understanding of key transactional data structures, document types, and master data entities within each module.

•     Familiarity with SAP data archiving and retention tooling, SAP ArchiveLink content repository integration, and the structure of SAP-generated audit and statutory reporting extracts.

•     Understanding of SAP integration middleware options including SAP BTP Integration Suite, SAP PI/PO, and third-party SAP ArchiveLink-certified connectors, and the ability to evaluate fit-for-purpose integration approaches for given enterprise contexts.

•     Awareness of SAP’s ECCend-of-mainstream-support timeline (2027) and the broader enterprise landscapeof SAP legacy modernisation, cloud migration, and compliance data preservationthat this creates for regional organisations.

4.5 Work Style & Soft Skills

•     Demonstrated ability to take fulltechnical ownership from design through production deployment and operationalsupport.

•     Comfortable working in fast-paced,ambiguous startup environments with evolving requirements and tight deliverytimelines.

•     Strong technical communicationskills: able to convey complex architectural decisions clearly to bothtechnical and non-technical stakeholders.

•     Effective cross-functionalcollaborator with experience coordinating geographically distributed,multi-cultural teams.

•     Proactive adopter of emergingtools and practices; applies pragmatic judgment in balancing innovation withdelivery risk.

5. Core Technology Stack

Category

Technologies

Languages

Java, Kotlin, Python, JavaScript, TypeScript, Scala

Backend & APIs

Spring Boot, Node.js, REST, Event-Driven Architecture, Microservices

Frontend

React, Next.js

AI & LLM Systems

Azure OpenAI (GPT-4), Azure AI Search (Vector), RAG Pipelines, Prompt Engineering, AI-Assisted Development (Vibe Coding)

Cloud – Azure

Blob Storage, Data Factory, Databricks, Synapse Analytics, OpenAI, AI Search, Event Grid, Key Vault, Entra ID, Monitor

Cloud – AWS

EC2, S3, Lambda, Redshift, SageMaker, Secrets Manager

Blockchain / DLT

Hyperledger Fabric (Chaincode – Go / Node.js), Hyperledger Besu, Smart Contracts, Permissioned Networks

SAP Integration

SAP ECC / S/4HANA, SAP ArchiveLink, SAP BTP Integration Suite, RFC/BAPI, IDoc, SAP FI / CO / MM / SD modules

Data & Storage

Apache Spark, PySpark, Delta Lake, Parquet, Oracle, MySQL, Postgres, MongoDB, Redis

DevOps & Infra

Docker, Kubernetes, GitHub Actions, Azure DevOps, Bicep, Terraform, CI/CD

Security

OAuth2, JWT, Entra ID SSO, RBAC, Row-Level Security, VNet Private Endpoints, Key Vault, AES-256