What are the responsibilities and job description for the Entry Level Hire Remote Technical Support - AI and Data Science Engineer and Support 2026 position at IBM?
Introduction
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.
Your Role And Responsibilities
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.
Preferred Education
Bachelor's Degree
Required Technical And Professional Expertise
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.
Your Role And Responsibilities
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.
Preferred Education
Bachelor's Degree
Required Technical And Professional Expertise
- 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.