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Build and deploy generative AI solutions using RAG, prompt engineering, and frameworks like LangChain, while ensuring compliance with AI governance and GDPR.
Leads AI agent design, testing, and deployment for a financial group, focusing on robust, safe workflows and LLM evaluation in Python and Azure.
Build and scale the data infrastructure that powers LLM training, fine-tuning, evaluation, and RAG systems using cloud tools like AWS Glue, EMR, and Kubernetes.
Build and maintain AI-powered document parsing pipelines, OCR/VLM systems, and RAG knowledge bases on Azure to extract and structure data from PDFs, images, and videos for GenAI applications.
Designs and implements enterprise data integrations and prepares data pipelines for GenAI initiatives, ensuring reliable data flow and quality across systems.
Location Singapore About the Role We are looking for a Full Stack Engineer – Generative AI & Agentic AI to design, develop, and deliver modern enterprise applications while leveraging Generative AI and Agentic AI…
Build and ship AI-powered applications end-to-end, integrating LLMs, vector databases, and retrieval pipelines into production systems with full-stack ownership.
Build and ship AI-powered applications using LLM APIs, vector databases, and retrieval-augmented generation with agentic orchestration.
Builds AI-powered interfaces for Eureka Materials, focusing on React/Vue frontends, data visualizations, and lightweight Node.js/Python backends to deliver AI Agent features and material-search workflows.
As a part of the Central AI-Core Studio & Delivery team, this role would require you to drive the design, build, and production delivery of AI-enabled applications for our core business lines. Key Responsibilities…
Lead the design and build of a Singapore government-wide search platform using GenAI, vector search, and cloud-native stacks (Node.js/TypeScript, Azure/AWS/GCP).
Builds and maintains a full-stack web app and integrates enterprise AI solutions using LLMs, RAG, and agentic frameworks in AWS.
Build and lead a GenAI-powered search platform using LLMs, RAG, and vector databases while mentoring engineers and defining scalable, secure architectures.
Builds full-stack AI features using LLMs, RAG, and agent workflows in Next.js, Python, and Node.js, ensuring low-latency, reliable AI integrations for production systems.
Build and own full-stack GenAI features using LLMs and RAG on AWS with TypeScript/Python, while driving engineering standards and mentoring peers.
Build and deploy generative-AI features and agents using Python, LLMs, and cloud services; develop full-stack web apps with Django/Flask and CI/CD pipelines.
Build and deploy AI/ML models and RAG systems in AWS GovCloud for secure government workflows, using Python and FedRAMP-authorized services while ensuring compliance and Zero Trust security.
Build and scale LLM-powered analytics features for healthcare data, including RAG, text-to-SQL, and agent workflows on AWS and Snowflake.
Build and deploy LLM-driven AI agents and generative solutions using Azure OpenAI, LangChain, and Hugging Face, focusing on RAG pipelines, multi-agent systems, and responsible AI.
Lead AI engineering and architecture for financial-services clients, designing GenAI and multi-agent systems, deploying RAG pipelines, and aligning solutions with EU regulations and enterprise standards.
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