Build, test, deploy, and maintain production full-stack applications , working across React and TypeScript frontends, Python services using FastAPI or Django, SQL databases, APIs, authentication, and system integrations.
Design and implement Agentic AI capabilities , including LLM orchestration, tool use, workflows, retrieval, structured outputs, and other patterns required to integrate generative AI into production applications.
Contribute to the evaluation of LLM and Agentic AI systems , helping define evaluation datasets and meaningful quality criteria, implementing automated evaluations, analysing failures and regressions, and using the results to improve system performance.
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Model Quality & Workflow Design: Design, manage, and optimize end-to-end workflows to improve machine model performance in content policy enforcement — including signal detection, rejection accuracy, and leakage reduction. Develop training data pipelines, QA processes, and performance tracking systems aligned to model improvement goals.
Adversarial Testing & Risk Identification: Conduct structured adversarial testing on AI models, features, and content policies to surface vulnerabilities, edge cases, and emerging risk trends. Explore model behaviour across contexts and user journeys to identify failure modes not captured in standard evaluations.
Root Cause Analysis & Error Optimization: Conduct structured root cause analysis (RCA) on model errors — including overkills, leakages, and misclassification — and translate findings into actionable model improvement recommendations. Partner with Algo and product teams to close root causes through memory insertion, threshold adjustments, rewrite rules, or policy iteration.
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Design and implement security controls for AI applications, LLM integrations, AI agents, and RAG-based systems
Identify and mitigate AI-specific threats such as prompt injection, jailbreaks, data exfiltration, model misuse, hallucination risks, and insecure tool/function calling
Secure prompt workflows, system prompts, agent instructions, retrieval pipelines, and AI orchestration layers
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We are looking for an AI Infra Engineer to support the development and operation of AI infrastructure platforms. The ideal candidate will have a strong background in Backend Engineering, Machine Learning Engineering, or MLOps, with hands-on experience in AI model training/inference deployment, cloud infrastructure, and GPU computing environments.
This role will work closely with algorithm teams to enable efficient utilization of AI computing resources, optimize AI workloads, and ensure reliable deployment of AI training and inference services.
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Drive Southeast Asia growth in a leading industrial materials business
Build key accounts across SEA markets
Reporting to the Overseas Sales Director, the Sales Engineer will be responsible for driving business development activities, managing customer relationships, and identifying growth opportunities within the Thailand or Vietnam market.
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Design and develop artificial intelligence (AI) and machine learning (ML) systems leveraging existing cloud AI services.
Design and build scalable data pipelines to support model training and production with DevOps & MLOps.
Customize and apply Deep Learning and Gen AI models for use cases based on the business needs, data availability, system and infrastructure requirements including edge devices and High Performance Computers (HPCs).
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We are looking for an AI Infra Engineer to support the development and operation of AI infrastructure platforms. The ideal candidate will have a strong background in Backend Engineering, Machine Learning Engineering, or MLOps, with hands-on experience in AI model training/inference deployment, cloud infrastructure, and GPU computing environments.
This role will work closely with algorithm teams to enable efficient utilization of AI computing resources, optimize AI workloads, and ensure reliable deployment of AI training and inference services.
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