About the Role
As a Machine Learning Engineer, you will leverage rich datasets at petabyte scale and state-of-the-art machine learning infrastructure to develop AI-driven products used by tens of millions of cryptocurrency users. You will collaborate closely with engineers, data analysts, business operations, and product managers to define and deliver solutions, features, algorithms, and products powered by advanced machine learning and data technologies.
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).
- Strong understanding of modern machine learning and deep learning techniques, including transformer architectures (BERT, GPT), hyperparameter optimization, and methods for handling imbalanced datasets.
- Experience optimizing and deploying ML models for low-latency inference in production, familiar with end-to-end ML deployment processes including version control (Git), continuous integration/continuous deployment (CI/CD), and managing multiple environments (dev, QA, staging, production).
Preferred
- Experience with productionising agentic AI systems or similar autonomous AI solutions is a plus.
- Prior experience in e-commerce or technology sectors is highly desirable.