Applied AI Engineer

PebbleRoad is a leading Singapore-based AI services company dedicated to orchestrating intelligent, end-to-end business workflows for large organizations across financial services, government, healthcare, and more. With over 20 years of experience, we move organizations past operational bottlenecks by building AI-native processes that transform complex, unstructured documents into reliable business data. Our approach, centered around services, solutions, and products like DocuPrism, delivers dramatic improvements in efficiency, governance, and speed across core business operations.We are now strategically expanding our team to scale our AI services and accelerate the development of our innovative AI products pipeline. Join us in shaping the future of intelligent automation.The roleWe are looking for an Applied AI Engineer to build practical AI capabilities for our product. The role focuses on the AI layer: designing AI workflows, selecting suitable models, evaluating output quality, and exposing AI features through APIs for our backend and frontend developers to consume. You will work closely with product managers, backend engineers, and frontend engineers to turn business problems into reliable AI-powered features.What you’ll doDesign and develop AI services for real product use casesBuild APIs for AI capabilities so they can be integrated into existing systemsDevelop OCR and document intelligence solutions with varying layoutsUse LLMs for tasks such as rewriting, summarisation, classification, and recommendationBuild recommendation logic based on user needs, pain points, and browsing behaviourEvaluate AI outputs for accuracy, relevance, consistency, and reliabilityCreate fallback logic, confidence scoring, and guardrails for uncertain AI outputsMonitor AI performance and improve models or prompts over timeWhat we’re looking forStrong experience in AWS stacksExperience working with LLMs, prompt engineering, structured outputs, and AI APIsExperience with OCR/document AI, especially extracting data from invoices, receipts, or formsUnderstanding of recommendation systems, ranking logic, embeddings, or similarity matchingExperience with structured data and basic data processingAbility to evaluate AI quality using test cases, benchmarks, and error analysisBasic understanding of cloud deployment, logging, and production reliabilityExperience with vector databases, RAG, LangChain, LlamaIndex, or Hugging FaceKnowledge of MLOps, CI/CD, Docker, or model monitoringExperience building AI features that have gone beyond proof-of-concept into productionBenefitsCompetitive compensation with performance-based bonusesMedical insuranceRemote-first work environmentOpportunity to work on real, enterprise-grade AI systems—not demosAccess to training and learning resources to deepen expertise in ML systems and infrastructure