Machine Learning Engineer

Singapore / Philippines

Remote • Independent Contractor

We are looking for a Machine Learning Engineer (AI / LLM Engineer) who is analytical, innovative, and deeply curious about how intelligent systems learn and improve. If you enjoy building, fine-tuning, and optimizing AI models, especially large language models, and turning research into production-ready systems, you will thrive in this role.

What You Will Do

As our Machine Learning Engineer, you will:

  • Design, develop, and deploy machine learning models for real-world applications
  • Fine-tune and optimize large language models (LLMs) for performance and accuracy
  • Build and maintain data pipelines for model training and evaluation
  • Implement prompt engineering strategies and LLM evaluation frameworks
  • Conduct experiments to improve model outputs, reliability, and efficiency
  • Monitor model performance and implement continuous improvements
  • Collaborate with data annotators and QA teams to refine training data
  • Optimize inference speed, scalability, and cost efficiency
  • Integrate AI models into web applications and internal tools
  • Document model architecture, workflows, and technical decisions

What We Are Looking For

You might be a great fit if you have:

  • Strong proficiency in Python with hands-on experience in PyTorch or TensorFlow
  • Solid understanding of deep learning fundamentals, including backpropagation, optimization algorithms, and loss functions
  • Strong knowledge of transformer architectures, attention mechanisms, and large language model internals
  • Experience fine-tuning LLMs using techniques such as LoRA, PEFT, instruction tuning, or supervised fine-tuning (SFT)
  • Hands-on experience designing and implementing retrieval-augmented generation (RAG) systems, including embeddings, vector databases, and retrieval pipelines
  • Experience working with embeddings and vector databases (e.g., Pinecone, Weaviate, FAISS) for semantic search and retrieval
  • Experience building evaluation frameworks for LLM outputs, including hallucination detection and response scoring
  • Familiarity with prompt engineering strategies and structured output control
  • Experience deploying models via APIs and optimizing inference performance (latency, throughput, and cost efficiency)
  • Knowledge of distributed training, GPU utilization, and model optimization techniques
  • Experience handling large-scale structured and unstructured datasets for training and evaluation
  • Strong debugging skills for model behavior analysis and performance bottlenecks
  • Ability to design experiments, interpret metrics, and iterate based on empirical results

Your Work Setup

  • Remote freelance work
  • 4 days per week, 12:00 AM to 9:00 AM (UTC+8)
  • Stable workload with structured experimentation and development cycles

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