United States Role overview
About this role
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, lets talk We are seeking a motivated and technically skilled early-career professional to join our AI and Data Science development team. As a Junior 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 with Senior Developers 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. • 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. • Familiarity with software engineering principles and debugging practices. • Strong analytical and problem-solving skills. • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow, Keras). • Exposure to model deployment using Docker, REST APIs, or cloud platforms (AWS, Azure, GCP). • Understanding of MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines). • Knowledge of distributed systems, storage architectures (e.g., IBM Storage Scale), and performance optimization. • Familiarity with Linux environments and container orchestration (e.g., Kubernetes, OpenShift). • Awareness of ethical AI principles, including fairness, transparency, and bias mitigation.