Job
Description
Solix Technologies Inc. is a leading provider of big data applications for enterprise archiving, data privacy, and advanced analytics. We are on a mission to help organizations manage and leverage their data for maximum value, efficiency, and compliance. Job Summary: We are seeking a highly skilled and motivated Machine Learning Engineer with hands-on experience in Natural Language Processing (NLP) and Large Language Models (LLMs). You will play a key role in designing, developing, and deploying scalable ML/NLP solutions that drive intelligent automation and data insight across our platforms. Key Responsibilities: Design and develop machine learning models, particularly in the domain of NLP and LLMs. Fine-tune, evaluate, and deploy transformer-based models (e.g., BERT, GPT, T5, LLaMA, etc.). Apply techniques such as named entity recognition (NER), text classification, semantic search, summarization, and question answering. Work with large-scale datasets to extract insights and build data pipelines. Collaborate with cross-functional teams including data engineers, product managers, and software developers. Conduct experiments, model training, and optimization to improve accuracy and performance. Stay up-to-date with the latest research in NLP, LLMs, and machine learning. Required Skills and Experience: Bachelor's or Masters degree in Computer Science, Data Science, AI/ML, or a related field. Minimum 5+ years of hands-on experience with NLP and LLMs Proficient in Python and ML frameworks like TensorFlow, PyTorch, Hugging Face Transformers. Strong understanding of modern NLP techniques (tokenization, embeddings, attention mechanisms, etc.). Experience with ML lifecycle including model development, evaluation, and deployment (MLOps). Familiarity with data handling libraries (Pandas, NumPy) and cloud platforms (AWS, GCP, or Azure). Good understanding of data preprocessing, feature engineering, and model validation techniques. Experience with open-source LLM fine-tuning and deployment. Knowledge of vector databases (e.g., FAISS, Pinecone) and retrieval-augmented generation (RAG). Prior experience with large-scale data systems or enterprise data environments. Published papers or open-source contributions in the ML/NLP space.
