Machine Learning Engineer

• 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.