Description
We are seeking an AIML Cloud Developer to join our team at Hewlett Packard Enterprise, specifically within Aruba, an HPE company. As an AIML Cloud Developer, you will design and develop AI/ML-based cloud application features, ensuring they meet specifications and adhere to security policies.
Responsibilities:
- Design simple to moderate AI/ML-based cloud application features as per specifications.
- Develop and maintain GenAI/cloud application modules, adhering to security policies.
- Design test plans, develop, execute, and automate test cases for assigned portions of the developed code.
- Deploy code and troubleshoot issues in application modules and the deployment environment.
- Share and review innovative technical ideas with peers, high-level technical contributors, technical writers, and managers.
- Analyze science, engineering, business, and other data processing problems to develop and implement solutions to complex application problems, system administration issues, or network concerns.
Requirements:
- Bachelor's degree in computer science, engineering, information systems, or a closely related quantitative discipline. Master's degree is desirable.
- Typically, 3-5 years of experience.
Knowledge and Skills:
- Strong working knowledge of GenAI, Prompts Engineering.
- Strong programming skills in Python (Pandas, Scikit, NumPy), Golang, and scripting.
- Hands-on experience with Jupyter notebooks.
- Experience with design methodologies, cloud-native applications, developer tools, managed services, and next-generation databases.
- Good written and verbal communication skills.
- Ability to quickly learn new skills and technologies and work well with other team members.
- Understanding of DevOps practices like continuous integration/deployment and orchestration with Kubernetes.
What We Can Offer You:
- Comprehensive suite of benefits supporting physical, financial, and emotional wellbeing.
- Investment in your career with programs to help you reach your career goals.
- Unconditional inclusion, celebrating individual uniqueness and valuing varied backgrounds.