AI Engineer

As an AI Engineer at Deriv, you will design and implement advanced AI solutions, integrating them across all operational facets, from trading algorithms to customer experience.

Key Responsibilities:

• Advanced Modeling: Develop and deploy deep learning, reinforcement learning, and graph neural networks for predictive analytics, automated trading strategies, and decision-making systems.
• NLP Applications: Implement state-of-the-art NLP solutions for sentiment analysis, document processing, and customer interaction enhancements using tools like spaCy, Hugging Face Transformers, and OpenAI APIs.
• Vector Search and Semantic Retrieval: Build systems utilizing vector databases like Weaviate, Pinecone, and Milvus to enable real-time, context-aware data retrieval.
• Agentic Systems: Design autonomous and multi-agent systems for dynamic decision-making and complex task management in trading environments.
• MLOps Integration: Deploy and maintain AI models at scale using tools such as MLflow, Kubeflow, TensorFlow Serving, and Seldon for seamless production workflows.
• Big Data Engineering: Architect high-performance data pipelines using Apache Spark, Kafka, and Hadoop for real-time and batch processing.
• Generative AI: Explore and integrate generative technologies, including GPT, DALL-E, and GANs, for innovative applications in user experience and content generation.
• Transformers and Architectures: Utilize advanced transformer models like BERT, T5, and ViT to solve complex problems in NLP and computer vision.
• Explainability and Fairness: Incorporate tools like SHAP, LIME, and Fairlearn to ensure AI systems are transparent, interpretable, and unbiased.
• Optimization: Use advanced hyperparameter tuning tools like Optuna and Ray Tune to maximize model performance.
• Cloud and Edge AI: Implement scalable AI systems on cloud platforms (AWS, Google Cloud, Azure) and optimize for edge computing with TensorFlow Lite and NVIDIA Jetson.

Requirements:

• Programming Expertise: Proficiency in Python, R, C++, or Java.
• Deep Learning Frameworks: Expertise in TensorFlow, PyTorch, and scikit-learn.
• Data Tools: Experience with Pandas, NumPy, and HDFS for data analysis and storage.
• Vector Databases: Knowledge of Weaviate, Pinecone, Milvus, or Annoy for similarity-based retrieval systems.
• Reinforcement Learning: Experience with tools like OpenAI Gym, Ray RLlib, and Stable Baselines.
• Generative AI Models: Familiarity with GANs, StyleGAN, BigGAN, and transformer-based models for various applications.
• MLOps and Automation: Proficiency in Docker, Kubernetes.