Software Engineer II - Generative AI & LLM Platforms

About the role

Hewlett Packard Enterprise is seeking a Software Engineer II (Junior Software Engineer) to support the design, development, testing and operation of secure and scalable Generative AI products and platforms. The role is hybrid, with about two days per week spent in an HPE office. The engineer works with experienced team members to build foundational skills in large language models (LLMs), retrieval-augmented generation (RAG), vector databases, cloud technologies, networking and distributed systems.

Key responsibilities

• Design limited enhancements, updates and programming changes for subsystems of systems software such as operating systems, compilers, networking, utilities, databases and internet-related tools.
• Analyze designs and determine the coding, programming and integration activities needed according to project guidelines.
• Write parts of testing plans, protocols and documentation, identify and debug code issues and suggest improvements.
• Work with other software engineers and outsourced partners to deliver reliable, cost-effective solutions, and share design status, progress and issue resolution.

Required profile

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence or a related discipline.
• Typically 2-4 years of relevant experience.
• Foundational software development knowledge from academic projects, internships, training or open-source contributions.
• Ability to work in an agile team, build simple prototypes and follow engineering, security and quality practices.

Required skills

• Basic proficiency in Python, Golang or a similar language, with knowledge of APIs, testing, debugging, version control and core software design.
• Awareness of Generative AI and LLM concepts (prompts, tokens, embeddings, inference).
• Basic cloud concepts, preferably AWS (compute, storage, IAM, monitoring, deployment).
• Foundational networking (TCP/IP, routing and switching, virtual networks, security) and systems concepts (scalability, reliability, performance, cost).

Preferred qualifications include AWS, Kubernetes, infrastructure as code, CI/CD, Amazon Bedrock, EMR, Spark, Flink, Aurora PostgreSQL, pgvector, EKS, S3, PyTorch, LangChain, LangGraph, LlamaIndex, MLOps/LLMOps, privacy-preserving AI, adversarial testing and AI red teaming.