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Artificial Intelligence Consultant

Salary undisclosed
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Simplified

Role : AI RAG Engineer (Retrieval-Augmented Generation - Chinese speaker)

Duration: Permanent

Budget : RM 15,000 + Benefits

Job Description:

We are seeking an experienced AI RAG Engineer with expertise in Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and Reinforcement Learning to develop and optimize AI-driven information retrieval systems. The ideal candidate will work on integrating large language models (LLMs) with structured and unstructured data sources, improving knowledge retrieval, and fine-tuning AI models to enhance response accuracy and efficiency.

Responsibilities

Develop and optimize RAG pipelines to enhance AI-driven search and retrieval systems.

Implement retrieval models using vector databases such as FAISS, Pinecone, Weaviate, and Milvus.

Fine-tune LLMs using supervised learning and reinforcement learning techniques like RLHF (Reinforcement Learning from Human Feedback).

Design and train embedding models to improve document retrieval and knowledge extraction.

Integrate structured and unstructured knowledge bases with AI systems to enhance response relevance.

Improve response accuracy and contextual coherence in AI-generated outputs.

Develop and optimize retrieval and ranking algorithms for real-time applications.

Leverage open-source AI models (e.g., Llama, Mistral, GPT, Claude) to enhance retrieval efficiency.

Optimize AI model deployment and improve training pipelines for production environments.

Required Skills & Qualifications:

Core AI & ML Skills

Experience with LLM fine-tuning and training (e.g., Hugging Face, OpenAI API, LangChain).

Strong understanding of Transformer models and NLP architectures.

Knowledge of vector embeddings, semantic search, and retrieval models (BM25, DPR, ColBERT, Hybrid Search).

Expertise in reinforcement learning and self-supervised learning.

Hands-on experience with RLHF (Reinforcement Learning from Human Feedback).

Role : AI RAG Engineer (Retrieval-Augmented Generation - Chinese speaker)

Duration: Permanent

Budget : RM 15,000 + Benefits

Job Description:

We are seeking an experienced AI RAG Engineer with expertise in Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and Reinforcement Learning to develop and optimize AI-driven information retrieval systems. The ideal candidate will work on integrating large language models (LLMs) with structured and unstructured data sources, improving knowledge retrieval, and fine-tuning AI models to enhance response accuracy and efficiency.

Responsibilities

Develop and optimize RAG pipelines to enhance AI-driven search and retrieval systems.

Implement retrieval models using vector databases such as FAISS, Pinecone, Weaviate, and Milvus.

Fine-tune LLMs using supervised learning and reinforcement learning techniques like RLHF (Reinforcement Learning from Human Feedback).

Design and train embedding models to improve document retrieval and knowledge extraction.

Integrate structured and unstructured knowledge bases with AI systems to enhance response relevance.

Improve response accuracy and contextual coherence in AI-generated outputs.

Develop and optimize retrieval and ranking algorithms for real-time applications.

Leverage open-source AI models (e.g., Llama, Mistral, GPT, Claude) to enhance retrieval efficiency.

Optimize AI model deployment and improve training pipelines for production environments.

Required Skills & Qualifications:

Core AI & ML Skills

Experience with LLM fine-tuning and training (e.g., Hugging Face, OpenAI API, LangChain).

Strong understanding of Transformer models and NLP architectures.

Knowledge of vector embeddings, semantic search, and retrieval models (BM25, DPR, ColBERT, Hybrid Search).

Expertise in reinforcement learning and self-supervised learning.

Hands-on experience with RLHF (Reinforcement Learning from Human Feedback).