IBM Infrastructure is a catalyst that makes the world work better because our clients demand it. Heterogeneous environments the explosion of data digital automation and cybersecurity threats require hybrid cloud infrastructure that only IBM can provide.
Your ability to be creative a forward-thinker and to focus on innovation that matters is all support by our growth minded culture as we continue to drive career development across our teams. Collaboration is key to IBM Infrastructure success as we bring together different business units and teams that balance their priorities in a way that best serves our client's needs.
IBM's product and technology landscape includes Research Software and Infrastructure. Entering this domain positions you at the heart of IBM where growth and innovation thrive.
We are seeking a motivated and technically skilled professional to join our AI and Data Science development team. As a Developer you will contribute to the design development and implementation of AI solutions and look for ways to effienciently collect clean analyze and visualize data to support business decisions that support real-world applications across enterprise systems. This role is ideal for someone with a strong foundation in machine learning and software engineering who is eager to grow in a collaborative innovation-driven environment. You will work in a global organization to build models helping to create predictive models generate insights and help optimize company performance.
- Proficiency in Python and experience with libraries such as NumPy pandas scikit-learn
- Proficiency in React and JS
- Solid understanding of machine learning algorithms and model evaluation techniques.
- Experience with Git and collaborative development workflows.
- Ability to work with structured and unstructured data including preprocessing and transformation.
- Experience with software engineering principles and debugging practices.
- Experience in model deployment using Docker REST APIs or IBM cloud
- Experience of MLOps tools and practices (e.g. MLflow Kubeflow CI/CD pipelines).
- Familiarity with Linux environments and container orchestration (e.g. Kubernetes OpenShift).
- Strong analytical and problem-solving skills.
• Experience with deep learning frameworks (e.g. PyTorch TensorFlow Keras).
• Knowledge of distributed systems storage architectures (e.g. IBM Storage Scale) and performance optimization.
• Awareness of ethical AI principles including fairness transparency and bias mitigation