Software Developer ELH

A career in IBM Software means you’ll be part of a team that transforms our customer’s challenges into solutions. Seeking new possibilities and always staying curious, we are a team dedicated to creating the world’s leading AI-powered, cloud-native software solutions for our customers. Our renowned legacy creates endless global opportunities for our IBMers, so the door is always open for those who want to grow their career. 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. In this role, you will: Contribute to the full software development lifecycle: design, implementation, testing, and deployment. Build and maintain Toolkits/backend services using Python. Develop and run containerized services using Docker and Kubernetes. Implement and integrate LLM-based agentic components, including tool-using and multi-step reasoning workflows. Participate in code reviews, design discussions, and Agile ceremonies. Learn through mentorship, technical deep dives, and continuous feedback. Who you are: You are early in your software career (0–1 year of professional experience) and excited to grow in backend engineering and AI systems. You are curious about how LLM-powered agents are built, evaluated, and improved, and you enjoy working with others to turn research ideas into production-quality software. Strong Python skills, and familiarity with high performance language such as Java. Basic knowledge of Docker and Kubernetes. Familiarity with  Machine Learning fundamentals and modern LLM-based agent architectures. Experience with Git and collaborative development workflows. Strong analytical, debugging, and problem-solving skills. Good written and verbal communication skills. Coursework, projects, or internships involving: LLM agentic systems (tool calling, planning, multi-agent coordination, retrieval-augmented generation). Agent evaluation: building test sets, automated metrics, trajectory comparison, or regression testing for LLM/agent behavior. Experience with PyTorch for training and inferencing. Building backend services with FastAPI, Flask, or Spring Boot. Basic CI/CD and automated testing. Working with vector databases and embedding models. Interest in reliability, monitoring, and performance evaluation of AI systems. United States Software Engineering Hybrid Entry Level Multiple Cities