Design and develop AI/ML solutions using supervised and unsupervised learning, deep learning, NLP, time series forecasting, and anomaly detection techniques to address business challenges.
Build Generative AI applications leveraging LLMs, prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), and AI agent frameworks.
Experience working in the banking domain.
Develop and maintain end-to-end AI pipelines, covering data ingestion, preprocessing, model training, deployment, monitoring, and continuous improvement.
Demonstrate strong programming expertise in Python and SQL, with hands-on experience in ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, SpaCy, and NLTK.
Work with large-scale data ecosystems, including ETL processes, data lakes, data warehouses, streaming platforms, and tools such as Spark, Databricks, or Microsoft Fabric.
Implement MLOps best practices, including CI/CD pipelines, model governance, explainability, monitoring, Docker-based containerization, and Kubernetes orchestration.
Deploy AI models through APIs and microservices, ensuring seamless integration with enterprise applications, systems, and cloud platforms.
Utilize cloud-based AI services on Azure or AWS, including platforms such as Azure Machine Learning and Amazon SageMaker.
Collaborate with business and technology stakeholders to translate business requirements into scalable AI solutions while tracking ROI and value realization.
Contribute to AI innovation and best practices through reusable frameworks, AI copilots, semantic models, knowledge graphs, LangChain/Semantic Kernel orchestration, and synthetic data techniques.