Experience must include: machine learning, Deep learning, Time-series analysis, and Business Intelligence across Telecom or Banking domains using Python (Scikit-learn, TensorFlow, or PyTorch); hands-on experience with LLMs, RAG systems, and NLP pipelines (HuggingFace, LangChain, LlamaIndex) alongside scalable MLOps/LLMOps on cloud platforms and AML or financial risk model development; GenAI agent development, LLM fine-tuning (SFT, RLHF, LoRA/PEFT), and Big data frameworks, such as Apache Spark. Leading multidomain predictive modeling frameworks leveraging deep learning, anomaly detection, and 5G network telemetry for real-time network optimization, while building AI-driven AML and financial risk models using graph neural networks and LLM-assisted transaction analysis to ensure BSA/AML regulatory compliance.