Our client, a leading trading broker, is looking to hire an AI Engineer to lead the development of sophisticated AI-driven systems to empower the business.
You'll be involved in
Advanced Modelling: Develop and deploy deep learning, reinforcement learning, and graph neural networks to enable predictive analytics, automated trading strategies, and decision-making systems.
NLP Applications: Implement advanced NLP solutions for sentiment analysis, document processing, and customer interaction improvements using tools such as spaCy, Hugging Face Transformers, and OpenAI APIs.
Vector Search and Semantic Retrieval: Build systems with 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 with tools like MLflow, Kubeflow, TensorFlow Serving, and Seldon, ensuring smooth production workflows.
Big Data Engineering: Architect high-performance data pipelines with technologies like Apache Spark, Kafka, and Hadoop for both real-time and batch processing.
Generative AI: Explore and integrate generative AI technologies, including GPT, DALL-E, and GANs, to drive innovation in user experience and content generation.
Transformers and Architectures: Utilize advanced transformer models such as BERT, T5, and ViT to solve complex challenges in NLP and computer vision.
Explainability and Fairness: Integrate tools like SHAP, LIME, and Fairlearn to ensure AI systems are transparent, interpretable, and fair.
Optimisation: Employ advanced hyperparameter tuning tools such as 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 solutions for edge computing with TensorFlow Lite and NVIDIA Jetson.
Requirements
Proficiency in Python, R, C++ or Java
Proficiency in Docker, Kubernetes, MLflow, Kubeflow
Deep Learning Frameworks: Knowledge in TensorFlow, Pytorch and scikit-learn
Data tools - Pandas, NumPy and HDFS experience
Vector databases: Weaviate, Pinecone, Milvus or Annoy experience
Reinforcement learning: OpenAI Gym, Ray RLlib or Stable baselines
Generative AI Models: familiarity with GANs, StyleGan, BigGan
Real-time processing: experience with flink, kafka and event stream processing
8 years of experience in AI
Education and Experience
Advanced degree in Computer science, Machine learning or related fields
