At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, let's talk.
In this role, you'll work in one of our IBM Consulting Client Innovation Centers (Delivery Centers), where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world. Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.
We are seeking a skilled Machine Learning Engineer to join our team for an exciting AI programme project with a leading banking client. The ideal candidate will be responsible for the design, development, and deployment of machine learning systems and applications.
Proven experience in deploying and maintaining AI algorithms in a corporate setup.
Proven experience in RAG system development.
Proven experience in prompt engineering.
Proven experience in data visualization and communication of results of analysis to stakeholders.
Proficiency in using GenAI cloud services.
Proficiency in programming in Python with common data science packages like Pandas, Numpy, Scikit-learn, Tensorflow or similar and experience with Natural Language Processing (NLP).
A working knowledge of relational database systems and ability to write complex queries.
Proven experience in deploying solutions in cloud infrastructure and sound knowledge of cloud technologies, container-based architectures and Linux systems.
Financial Background
Expertise in Asset Classes, Money Markets
Experience in analytics and ML product management.
Experience with analytics in the context of financial and economic datasets.
Experience in automation solutions for model training, experiment tracking, model deployment, monitoring and model retraining;
Experience with a CI/CD toolchain.
Hands-on knowledge in managing and securing APIs.
Knowledge of software architecture patterns for machine learning.