Job description for Machine Learning Engineer at PT Prima Vista Solusi
- Design, develop, and deploy cutting-edge LLMs for diverse NLP applications.
- Optimize and fine-tune large-scale language models using frameworks like TensorFlow, PyTorch, or JAX.
- Research and implement advancements in LLMs, including transformer architectures and reinforcement learning techniques.
- Work closely with data scientists, software engineers, and product managers to integrate LLMs into production systems.
- Build and maintain scalable machine learning pipelines for training and inference.
- Ensure LLMs are efficient, high-performing, and scalable for real-world applications.
- Evaluate AI models using key NLP metrics and refine them based on experimental results.
- Stay updated with the latest AI research and contribute to open-source projects where relevant.
- Implement responsible AI principles, including fairness, explainability, and ethical considerations.
- Document AI model architectures, training methodologies, and deployment strategies.
Qualifications:
- Bachelor's, Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- Minimum 3-5 years of experience in machine learning, deep learning, or natural language processing roles.
- Strong proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, or JAX.
- Hands-on experience with large-scale language models, including transformers, GPT-based models, and BERT-style architectures.
- Experience with cloud platforms (AWS, Google Cloud, Azure) and ML model deployment.
- Knowledge of MLOps practices, including model versioning, monitoring, and automation.
- Experience with large-scale datasets, data preprocessing, and distributed computing frameworks (e.g., Spark, Ray).
- Solid understanding of deep learning architectures, optimization techniques, and reinforcement learning.
- Strong problem-solving skills and ability to conduct independent research.
- Excellent collaboration and communication skills for cross-functional teamwork.
- Experience with prompt engineering, fine-tuning LLMs, and retrieval-augmented generation (RAG) is a must.
- Contributions to AI research publications or open-source projects are highly desirable.
