Full-Stack Developer Fintech, Data & AI

Senior AI & Full-Stack Engineer - Fintech

Company:

Smart Towkay Ventures Pte. Ltd. Location:

Singapore Employment Type:

Full-time Seniority:

Senior / Lead Industry:

Fintech, SME Financing, Data and Artificial Intelligence About Smart Towkay Ventures

Smart Towkay Ventures Pte. Ltd. develops AI-driven technology and digital platforms for Singapore's SME financing ecosystem, including the SmartLend financing marketplace. We enables businesses to submit one application and compare financing options from multiple banks and financial institutions. Our platform processes substantial volumes of financial and business data, including bank statements, corporate records, credit reports, financing applications, supporting documents and lender decisions. We are building an intelligent financing platform that uses data and AI to automate financial analysis, detect risks, match borrowers with suitable lenders and improve financing outcomes for Singapore SMEs. The Role

We are looking for a Senior AI & Full-Stack Engineer to lead the development and production deployment of AI capabilities across our fintech platforms. This role combines AI engineering, data engineering and full-stack development. You will build intelligent systems that can extract, classify and analyse financial information, identify credit and fraud indicators, recommend suitable lenders and automate parts of the financing workflow. You will also be responsible for integrating these capabilities securely into SmartLend's borrower, lender and internal operations platforms. The ideal candidate is not limited to experimenting with AI models. You must be able to turn AI concepts into secure, reliable and explainable production features that deliver measurable business value. Key Responsibilities

AI Product Development

Identify and develop practical AI use cases across SmartLend's financing workflow.

Build AI-assisted tools to automate the review of bank statements, credit reports, corporate records and supporting documents.

Develop systems that extract, classify, summarise and validate information from structured and unstructured documents.

Build intelligent lender-matching capabilities based on borrower profiles, lender criteria and historical outcomes.

Develop approval-probability and financing-amount recommendation models.

Create AI-generated credit summaries and preliminary risk assessments for internal users and lenders.

Develop fraud and anomaly-detection tools to identify unusual transactions, document inconsistencies and potentially manipulated information.

Build internal AI assistants to help employees search case information, interpret financial data and prepare lender submissions.

Introduce human-review and approval controls for high-impact AI recommendations.

Continuously measure the accuracy, reliability, speed and business impact of deployed AI features.

Generative AI and Large Language Models

Build and deploy applications using large language models and generative AI.

Design effective prompts, structured outputs, tool-calling workflows and AI agents.

Develop retrieval-augmented generation systems using SmartLend's internal knowledge and financing criteria.

Implement vector search, document chunking, embeddings and knowledge-retrieval pipelines.

Evaluate and integrate suitable commercial or open-source AI models.

Establish safeguards to reduce hallucinations, data leakage and unreliable outputs.

Develop model-evaluation datasets and testing frameworks for AI-generated results.

Monitor model quality, cost, latency and performance in production.

Maintain appropriate model and vendor flexibility to avoid unnecessary dependence on one provider.

Financial Data Processing

Design scalable pipelines for processing bank statements, credit reports, corporate information and financing-application data.

Clean, standardise, reconcile and structure financial data received from multiple sources.

Develop transaction-categorisation and cash-flow analysis capabilities.

Identify recurring revenue, major customers and suppliers, monthly commitments, unusual transactions and risk indicators.

Build features to calculate and analyse financial metrics relevant to credit assessment.

Develop processes to detect missing, inaccurate or conflicting information.

Create management dashboards covering borrower profiles, application conversions, lender performance and financing outcomes.

Maintain high standards of data accuracy, completeness and traceability.

Machine Learning

Develop and evaluate classification, regression, recommendation and anomaly-detection models.

Prepare and manage training, validation and testing datasets.

Perform feature engineering using financial, corporate, transactional and application data.

Build models for lender matching, approval prediction, fraud detection and credit-risk assessment.

Establish model versioning, validation, deployment and monitoring processes.

Detect model drift, performance degradation and potential bias.

Ensure that important model outputs are explainable to management, operations teams and lenders.

Maintain human oversight for financing decisions and other high-impact recommendations.

Full-Stack and Platform Development

Design, develop and maintain borrower, lender, partner and internal operations platforms.

Build secure and scalable backend services, APIs, databases and workflow engines.

Develop responsive interfaces and dashboards for AI-assisted financial analysis.

Integrate AI capabilities directly into existing application and approval workflows.

Build APIs and microservices to serve AI models and data-processing functions.

Integrate identity, corporate-data, document-processing, e-signature and financial-data services.

Monitor application performance, system availability and production errors.

Maintain clear documentation for system architecture, APIs, data flows and AI components.

AI Governance, Security and Data Protection

Ensure financial and personal information is handled securely throughout the AI lifecycle.

Implement role-based access controls, encryption, secure storage and secrets management.

Maintain comprehensive audit trails showing the data, model and rules used to generate important outputs.

Anonymise or mask sensitive data where appropriate for testing, analytics and model development.

Prevent confidential customer information from being exposed to unauthorised third-party AI services.

Establish policies for AI access, acceptable use, output verification and human approval.

Test AI systems for hallucinations, prompt injection, data leakage and other security risks.

Ensure development, testing and production environments are properly separated.

Support cybersecurity assessments, penetration testing and incident response.

Consider applicable data-protection, fairness and explainability requirements when designing AI systems.

MLOps and Development Operations

Build reliable deployment pipelines for AI models and software releases.

Implement model registries, experiment tracking, version control and rollback procedures.

Establish automated testing for code, data pipelines, prompts and model outputs.

Monitor model accuracy, latency, usage, infrastructure cost and failure rates.

Develop suitable logging, alerting and incident-management processes.

Optimise AI infrastructure and model usage for performance and cost efficiency.

Work with external developers, AI specialists, cybersecurity consultants and technology vendors where required.

Core Requirements

Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering or a related discipline, or equivalent practical experience.

At least five years of relevant software-development, data-engineering or AI-engineering experience.

Demonstrated experience deploying AI or machine-learning capabilities into production applications.

Strong proficiency in Python and commonly used data-processing libraries.

Strong experience with SQL, relational databases, data modelling and query optimisation.

Experience with machine-learning frameworks such as scikit-learn, TensorFlow, PyTorch or equivalent tools.

Practical experience using large language models, AI APIs, embeddings, vector databases or retrieval-augmented generation.

Strong backend-development experience using Python, Node.js, Java, .NET or another modern technology.

Experience with frontend frameworks such as React, Next.js, Vue or Angular.

Experience designing APIs, webhooks and third-party integrations.

Experience deploying and maintaining production applications on AWS, Azure, Google Cloud or an equivalent cloud platform.

Understanding of authentication, authorisation, encryption, secure coding and secrets management.

Experience with Git, automated testing, CI/CD, logging and application monitoring.

Ability to explain AI systems, model limitations and technical risks clearly to non-technical stakeholders.

Strong ownership, analytical and problem-solving capabilities.

Preferred Experience

Experience in fintech, banking, lending, insurance, payments or another sensitive-data environment.

Experience with SME financing, credit assessment, loan origination or lender decisioning.

Experience analysing bank statements, credit reports or transactional data.

Experience with OCR, document intelligence and financial-document extraction.

Experience building recommendation systems, fraud-detection models or credit-risk models.

Familiarity with Singapore's PDPA and good practices for protecting personal and financial data.

Experience with Singpass/MyInfo, MyInfo Business or similar identity and corporate-data integrations.

Experience building AI agents, workflow automation or internal AI copilots.

Experience managing large datasets, data warehouses and ETL or ELT pipelines.

Familiarity with MLOps, model monitoring and AI-governance practices.

Experience leading developers, AI engineers or external technology vendors.

Direct fintech experience is preferred but not compulsory. Strong candidates from banking, insurance, healthcare, government technology or other sensitive-data industries are welcome. Priority AI Projects

The successful candidate will be expected to contribute to projects such as: AI bank-statement analysis and transaction categorisation.

Automated extraction and validation of financial documents.

AI-generated borrower and credit summaries.

Rule-based and AI-assisted lender matching.

Financing approval-probability prediction.

Fraud and manipulated-document detection.

Internal AI assistant for case analysis and lender submissions.

Searchable lender-policy and financing-criteria knowledge base.

Conversion, lender-performance and portfolio analytics.

Automated alerts for missing information, unusual transactions and potential risks.

What Success Looks Like

Within the first six months, the successful candidate should be able to: Understand and document SmartLend's architecture, financial-data flows and existing AI capabilities.

Establish a prioritised AI roadmap based on business value, technical feasibility and risk.

Build scalable and reliable financial-data processing pipelines.

Launch at least one meaningful AI capability into production.

Improve the speed and consistency of bank-statement or financing-application analysis.

Introduce measurable standards for AI accuracy, reliability, cost and human review.

Strengthen data security, auditability and AI-governance controls.

Establish a reliable process for testing, deploying and monitoring AI systems.

Present clear recommendations for the platform's longer-term data and AI architecture.

What We Offer

A key AI and technology role in a growing Singapore fintech business.

Direct involvement in product, architecture, data and AI strategy.

Access to real-world SME financing and financial-transaction datasets.

Opportunities to build practical AI products with direct commercial impact.

Exposure to banks, alternative lenders and financial-data providers.

Career progression towards AI Lead, Head of Engineering, Head of AI responsibilities based on performance and company growth.

Application Requirements

Please submit your résumé together with: Links to your GitHub profile, portfolio or relevant production projects.

A description of an AI or machine-learning system you have deployed in production.

Details of your contribution to its architecture, model development and deployment.

Examples of data-processing, document-intelligence or financial-technology projects.

The AI models, frameworks and cloud technologies you have worked with.

Your current and expected salary.

Your notice period and earliest available starting date.

Shortlisted candidates may be asked to complete a practical assessment involving financial-data processing, AI-system design and secure production deployment.