Data Science Engineer NLP P2P Content

Responsibilities:

Apply NLP techniques to preprocess and analyze large-scale textual data, developing and fine-tuning Large Language Models (LLMs) and multimodal models to generate actionable business insights. Design, build, and maintain end-to-end machine learning pipelines—including data ingestion, cleaning, feature engineering, model training, evaluation, deployment, and monitoring Lead the deployment of ML models in production environments with a focus on scalability, reliability, availability, and low-latency inference, leveraging cloud infrastructure for optimal performance. Collaborate with business and technical stakeholders to identify AI opportunities, align initiatives with organizational goals, and communicate insights effectively through data analysis and visualization. Stay abreast of the latest AI advancements, particularly in multimodal AI, to continuously integrate cutting-edge technologies into solutions. Explore the use of agentic AI to automate detection and monitoring within risk management systems, improving accuracy and response times.

Requirements:

Minimum 4 years of industry experience in AI/ML, preferably focused on NLP and/or multimodal AI, with a Master's degree or higher in Computer Science, Data Science, or related fields. Proficient in big data technologies (e.g., Apache Spark, Hadoop, Kafka, VectorDB) or equivalent platforms. Skilled in programming languages such as Python or Java, with hands-on experience in ML/NLP libraries and deep learning frameworks (TensorFlow, PyTorch, Scikit-learn, SpaCy, NLTK). • Looking for your next challenge? The world of crypto offers exciting roles in blockchain development, web3 innovations, and remote opportunities. Apply tot his job