Entry Level AI Software Engineer

About the Role

At IBM Software, we transform client challenges into solutions, building AI-powered, cloud-native products that shape the future of business and society. This role involves building AI for systems that support transactions and data for banking, payments, and global commerce. As an Entry Level AI Software Engineer, you will work alongside experienced engineers to turn advances in large language models into useful software. You will help take a feature from prototype through testing and refinement.

Key Responsibilities

• Design, build, and test AI-powered features with guidance.
• Integrate LLMs, APIs, and enterprise data into applications, exploring agent workflows, tool calling, and retrieval.
• Write clear specifications and effective prompts, use AI development tools, and review/test code.
• Evaluate AI behavior with realistic test cases, measure quality and performance, and improve handling of incorrect outputs and failures.
• Debug across model interactions, application code, APIs, and underlying systems.
• Collaborate through Git, code reviews, and technical discussions; document work and iterate on feedback.

Requirements & Tech Stack

• Strong software engineering fundamentals, including data structures, algorithms, software design, and testing.
• Proficiency in Python and experience building applications or services that integrate APIs.
• Hands-on experience building an application or working prototype with LLMs.
• A systematic debugging mindset.
• Ability to translate a problem into clear requirements, prompts, and testable acceptance criteria.
• Familiarity with Git, clear technical communication, and the ability to learn quickly and collaborate.

Qualifications

• Bachelor's Degree.
• Experience building an application or working prototype with LLMs. This experience can come from personal, academic, open-source, or internship projects.

Preferred Technical And Professional Experience

Experience in one or more of the following areas is a plus. You do not need experience with every technology listed.

• AI agents and tool or function calling; retrieval-augmented generation (RAG), embeddings, vector databases, or Model Context Protocol (MCP).
• Independent AI projects, open-source contributions, or experiments beyond assigned coursework.
• Additional programming experience in JavaScript/TypeScript or Go.
• Familiarity with distributed systems or microservices.
• Operating system concepts such as concurrency, multithreading, and memory management.
• Relational databases and SQL.
• Cloud and container technologies such as Docker, Kubernetes, or OpenShift.
• Observability, search, or real-time data processing tools such as OpenSearch or Kafka.
• AI evaluation, anomaly detection, or experience measuring and improving model accuracy, latency, and reliability.

We are especially interested in what you have built and learned. Where available, include a project link, repository, demo, or a brief description of your contribution and the technical challenges you worked through.