jobs in USER EXPERIENCE RESEARCHERS PTE. LTD.

全职 AI Engineer 工作, 薪水 up to SGD 9,500, USER EXPERIENCE RESEARCHERS PTE. LTD. North Region (Singapore) 公司招聘中 - Ricebowl

AI Engineer

USER EXPERIENCE RESEARCHERS PTE. LTD.

North Region (Singapore)

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工作地点

  • 50 GAMBAS CRESCENT North Region (Singapore) Singapore

职位描述

岗位职责

We are looking for an AI Engineer to design, develop, and deploy AI-powered applications and machine learning solutions. The role involves building LLM-based features, integrating AI models into production systems, and collaborating with software, data, and product teams to deliver scalable AI products.

Key Responsibilities

  • Design, develop, and deploy AI/ML applications for production environments.

  • Build and integrate Large Language Model (LLM) solutions using APIs and open-source models.

  • Develop RAG (Retrieval-Augmented Generation) pipelines with vector databases.

  • Create and optimize prompts, AI agents, and workflow automation.

  • Fine-tune and evaluate machine learning and generative AI models.

  • Build scalable REST APIs and integrate AI services with existing applications.

  • Monitor model performance, improve accuracy, and optimize inference costs.

  • Collaborate with cross-functional teams to translate business requirements into AI solutions.

Requirements

  • Bachelor's degree in Computer Science, AI, Data Science, or a related field.

  • 3+ years of experience in AI, Machine Learning, or Generative AI development.

  • Strong proficiency in Python.

  • Experience with LLM frameworks (LangChain, LlamaIndex, or similar).

  • Hands-on experience with OpenAI, Azure OpenAI, Claude, or open-source LLMs.

  • Knowledge of RAG, embeddings, and vector databases (Pinecone, Milvus, FAISS, ChromaDB).

  • Experience with PyTorch or TensorFlow.

  • Familiarity with Docker, Git, CI/CD, and cloud platforms (AWS, Azure, or GCP).

  • Good understanding of API development and system integration.

Preferred Skills

  • Experience with AI agents and multi-agent architectures.

  • Knowledge of MLOps tools (MLflow, Weights & Biases, Kubeflow).

  • Experience deploying models on Kubernetes or cloud-native environments.

  • Understanding of NLP, computer vision, or speech AI is an advantage.

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