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).
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