Design, train, and deploy ML models across use cases (rec, churn, fraud, behavior)
Build modular ML pipelines for feature engineering, training, validation, and inference
Implement deep learning and NLP models, incl transformer architectures
Develop and deploy LLM-based applications (summarization, chat, automation)
Integrate models into production via REST/gRPC APIs and microservices
Implement MLOps pipelines with CI/CD, versioning, and monitoring

🎯 Requirements

Strong Python with ML libraries: scikit-learn, XGBoost, LightGBM
TensorFlow, PyTorch
Hugging Face Transformers
AWS SageMaker, Bedrock, Lambda/ECS/EKS
NLP/LLMs or time-series modeling
MLOps tools: MLflow, Kubeflow, Airflow; Docker, Kubernetes

🎁 Benefits

Work with leading financial derivatives institution
Competitive salary with incentives
International, fast-growing environment
Career progression within a global financial group
Innovative, growth-focused team
Global, diverse international community

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