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If you’re looking for a career where you can make a real impression, join HSBC and discover how valued you’ll be.

We are currently seeking an experienced professional to join our team in the role of Consultant Specialist.

Business: CTO Infrastructure

Principal responsibilities

1. Build real-time anomaly detection and prediction models using Transformers, LSTM, and time-frequency fusion.
2. Construct knowledge graphs and apply causal inference (PC/Bayesian networks, GNNs) for root cause analysis.
3. Deploy models in real-world scenarios (e.g., equipment monitoring, fault diagnosis, e-commerce optimization) using XGBoost/LightGBM, KDE, and LSTM.
4. Design and execute A/B tests to evaluate model impact with metrics (accuracy, F1, AUC-ROC) and business KPIs.
5. Develop and deploy LLM & RAG solutions for intelligent Q&A, knowledge retrieval, and automated reasoning, integrating vector databases (e.g., FAISS, Milvus).
6. Integrate RAG with knowledge graphs to enhance retrieval accuracy and provide context-aware, explainable answers for complex business scenarios.

1. Modeling: Transformers, LSTM, XGBoost/LightGBM, KDE.
2. Causal Inference: PC/Bayesian networks, GNNs, knowledge graphs.
3. LLM & RAG: Experience with LLMs (e.g., GPT, Llama), RAG frameworks (LangChain, LlamaIndex), vector search (FAISS, Milvus), and prompt engineering
4. Knowledge Graph Integration: Practical experience combining RAG with knowledge graphs for enhanced retrieval and reasoning
5. Evaluation: A/B testing, model metrics (F1, AUC), feature analysis (SHAP).
6. Tools: TensorFlow/PyTorch, Spark/Flink/Kafka, model serving (e.g., TensorFlow Serving).
7. Deployment: Experience with distributed computing and model integration into production systems.
8. Anomaly Detection Project Experience: Proven track record in deploying models for industrial equipment monitoring, financial system fault diagnosis, or e-commerce platform performance optimization.
9. Causal Root Cause Analysis Project Experience: Hands-on experience in building knowledge graphs and applying causal inference methods (e.g., PC/Bayesian networks) to identify root causes in complex systems.