Experience working in the banking industry is required/preferred.
Develop end-to-end AI pipelines
Data ingestion
Data preprocessing
Model training
Deployment
Monitoring
Continuous improvement
Strong programming expertise
Python
SQL
Scikit-learn
TensorFlow
PyTorch
Hugging Face
spaCy
NLTK
Large-scale data ecosystem experience
ETL processes
Data lakes
Data warehouses
Streaming platforms
Spark
Databricks
Microsoft Fabric
MLOps best practices
CI/CD pipelines
Model governance
Explainability
Monitoring
Docker
Kubernetes
Model deployment
APIs
Microservices
Enterprise application integration
Cloud AI services
Azure Machine Learning
Amazon SageMaker
Azure or AWS cloud platforms
Business collaboration
Translate business
requirements
into scalable AI solutions • Measure ROI and business value • AI innovation • Reusable AI frameworks • AI copilots • Semantic models • Knowledge graphs • LangChain / Semantic Kernel orchestration • Synthetic data techniques Overall Assessment This role is aimed at a Senior AI Engineer / GenAI Engineer with expertise in: • Generative AI and AI Agents • RAG architecture • Python and ML frameworks • MLOps and cloud deployment • Banking domain knowledge • Azure/AWS ecosystems • LangChain and Semantic Kernel