Senior AI/ML Engineer

Job Title: Senior Machine Learning Engineer – NLP & LLMLocation: Gaushala, Kathmandu Experience: 5+ years in Machine Learning / NLP Job Type: Full-timeAbout Us:Cubit is at the forefront of AI innovation, building state-of-the-art solutions that leverage natural language understanding to transform.

We are looking for a highly skilled Senior ML Engineer with deep expertise in NLP and large language models (LLMs) to join our AI team.Role Overview:As a Senior ML Engineer, you will design, develop, and deploy advanced NLP models, including large language models, for real-world applications. You will collaborate with product, data, and engineering teams to drive AI solutions that scale efficiently and deliver measurable impact.

Key Responsibilities

Develop, fine-tune, and deploy large-scale NLP models (transformers, LLMs) for diverse applications such as text generation, summarization, classification, and information retrieval.Research, experiment, and implement state-of-the-art techniques in NLP and deep learning.Optimize model performance, efficiency, and inference for production deployment.Build data pipelines for large-scale text datasets and ensure high-quality training data preparation.Collaborate with cross-functional teams to translate business requirements into ML solutions.Stay up-to-date with latest research in NLP, LLMs, and AI technologies.Required Qualifications:5+ years of experience in ML/NLP engineering with production-level deployments.Strong expertise in transformer-based models and LLMs.Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.Proficiency in Python and ML libraries (Hugging Face Transformers, spaCy, etc.).Experience in model fine-tuning, prompt engineering, and handling large-scale datasets.Strong understanding of ML infrastructure, GPU/TPU optimization, and scalable deployment.Solid grasp of NLP concepts including embeddings, attention mechanisms, and sequence modeling.

Preferred Qualifications

Experience with Reinforcement Learning from Human Feedback (RLHF) or instruction-tuned models.Familiarity with cloud ML platforms (AWS SageMaker, GCP AI Platform, Azure ML).Knowledge of MLOps, model monitoring, and continuous training pipelines.Open-source contributions in NLP/LLM communities.