We are looking for a Senior Machine Learning Engineer with deep expertise in Transformers, Large Language Models (LLMs), and Natural Language Processing (NLP). This role involves designing, training, and fine-tuning state-of-the-art AI models for real-world applications. The ideal candidate will have a strong research background and hands-on experience in deploying scalable NLP solutions. Key Responsibilities Research, develop, and optimize Transformer-based architectures (e.g., BERT, GPT, T5, LLaMA) for various NLP tasks. Fine-tune LLMs on domain-specific datasets to improve accuracy and performance. Work on text generation, summarization, named entity recognition (NER), and semantic search applications. Implement and optimize embedding techniques for retrieval-augmented generation (RAG). Apply self-supervised and reinforcement learning techniques to enhance model performance. Deploy and scale ML models using cloud platforms (AWS, GCP, Azure) and containerized solutions like Docker and Kubernetes. Improve inference efficiency using quantization, distillation, and model optimization techniques. Collaborate with data engineers, software developers, and research scientists to integrate ML models into production. Stay updated with the latest advancements in AI, NLP, and Deep Learning, applying innovative techniques to solve business challenges. Required Skills & Qualifications Expertise in NLP & LLMs: Strong understanding of transformer-based models (e.g., BERT, GPT, T5, LLaMA). Programming Skills: Proficiency in Python and deep learning frameworks like PyTorch, TensorFlow, and Hugging Face Transformers. Model Optimization: Experience with quantization, pruning, and distillation to improve model efficiency. Data Handling: Strong experience in preprocessing, tokenization, and vectorization of large text datasets. Deployment & Scalability: Hands-on experience with MLOps, API development, cloud services (AWS, GCP, Azure), and containerization (Docker, Kubernetes). Information Retrieval & RAG: Knowledge of vector databases (FAISS, Pinecone, Weaviate) and embedding techniques. Mathematical Foundation: Strong background in linear algebra, probability, and deep learning architectures. Collaboration: Ability to work with cross-functional teams and communicate technical concepts effectively. Preferred Qualifications Experience in low-rank adaptation (LoRA) and fine-tuning LLMs with limited resources. Exposure to multimodal learning (text, images, audio). Research publications or contributions to open-source NLP projects. Familiarity with prompt engineering and fine-tuning for AI assistants. What We Offer Opportunity to work on cutting-edge AI and NLP projects with a talented team. Ability to shape the development of next-generation AI applications. Access to latest ML research, conferences, and learning resources. Flexible work arrangements (remote/hybrid options available). Competitive salary and performance-based incentives.
