Editorial summary

The company is seeking an AI Full Stack Engineer for a banking project on a one-year renewable contract, offering a salary of 11,000. The role involves analyzing business and technical requirements, designing data flows, and implementing data integration solutions using REST APIs, SFTP, and batch pipelines. Key responsibilities include defining data contracts, interfaces, and mappings to ensure reliable data movement across enterprise systems. Candidates are instructed to submit their CVs via email, with no visa sponsorship available.

This summary is AI-generated and may contain inaccuracies. Please refer to the full job description below.

Job description

[This job https://singapore.job-q.com/jobs/detail/ai-full-stack-engineer-banking-1-year-renewable-contract-36734 first appeared in Job-Q.com on 08 Oct 2026]

Dear Applicant,

If you or someone you know is interested, please send the CV directly to (most preferred, as I may overlook some CVs due to the high volume).

Please note that visa sponsorship is not available at this time.

Key Responsibilities

• Analyse business and technical requirements and translate them into data flows, integration designs and implementation plans.
• Design and implement reliable data movement across upstream and downstream enterprise systems using REST APIs, SFTP, file-based transfers and batch pipelines.
• Define data contracts, interfaces, mappings and transformation requirements across multiple systems and teams.
• Develop scripts, programs and APIs to extract, transform and integrate data from different systems.
• Coordinate integrations across enterprise data platforms and data lake environments.
• Ensure data is accurately transformed, mapped, aggregated and delivered to target systems with appropriate data quality and reconciliation controls.
• Support GenAI use cases by preparing and integrating structured and unstructured data for document ingestion, enrichment, search and RAG workflows.
• Troubleshoot integration and data movement issues across development, testing and production environments.
• Work closely with application, data platform, infrastructure and security teams to deliver end-to-end solutions.
• Challenge inefficient or unsuitable designs and propose practical, scalable and maintainable solutions.
• Support SIT, UAT and production deployments, ensuring integration reliability, error handling and monitoring.
• Document system flows, data mappings, interfaces and integration processes clearly.
• Provide production support, including incident investigation, troubleshooting, bug fixes, code corrections and application enhancements.
• Troubleshoot across the technology stack, including Python, Celery, databases, Docker, Kubernetes/OpenShift and CI/CD.
• Implement technical enhancements and continuous improvements arising from production issues and operational requirements.

Key Requirements

• 5–10 years of experience in system analysis, integration engineering, data engineering or technical delivery roles.
• Strong Python and SQL skills for data handling, scripting, automation and troubleshooting.
• Good understanding of frontend technologies such as HTML and Jinja templating.
• Hands-on experience with Celery for asynchronous/background task processing.
• Strong ability to translate requirements into system flows, data flows, interface specifications and implementation plans.
• Practical experience with REST APIs, SFTP, batch processing, file-based integration and data pipeline orchestration.
• Strong understanding of data mapping, transformation, aggregation, reconciliation and data quality controls.
• Experience working across upstream and downstream teams to deliver enterprise integrations.
• Exposure to Java and enterprise data platforms such as Cloudera.
• Hands-on experience with Git, branching, pull requests, code reviews and controlled release practices.
• Familiarity with CI/CD, Jira, Confluence and enterprise deployment processes.
• Experience deploying and supporting applications on Kubernetes and OpenShift (OCP).
• Experience with Control-M or equivalent scheduling tools.
• Familiarity with logging and monitoring tools such as Splunk and Elastic Stack.
• Exposure to GenAI concepts, including document ingestion, RAG, embeddings and data preparation for AI workflows.
• Strong communication and stakeholder management skills, with the ability to work across business, application, data, infrastructure and security teams.
• Strong system-thinking and problem-solving skills, with the ability to navigate complex enterprise environments and challenge weak designs.

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