AI Solution Engineer
United States
Full-time
1 week ago
$172,000 - $349,000 USD
H1B Sponsor Likely
Key skills
Machine LearningDeep LearningKubernetesPythonAI TechnologiesML Code WritingUnix-like SystemsPrototypingCommunicationContinuous LearningAIArtificial IntelligenceMLLLMPyTorchLangChainData EngineeringSales
About this role
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. As an AI Solution Engineer, you will architect, build, and deliver AI solutions for customers during the pre-sales stage, working closely with sales and partners to implement proof-of-concepts that can be expanded to production deployments.
Responsibilities:
- Lead technical discussions with prospects and partners to propose HPE and partner Integrated solutions that address business challenges and opportunities using AI
- Demo AI solutions (either existing or built by you) to prospects that address their use cases or desired AI outcomes
- Lead Proof-of-Concepts / Proof-of-Value engagements for HPE prospects that demonstrate clear value from HPE's AI offerings, likely in combination with 3rd Party and Open Source components
- Assist in any product or technical issue towards an initial sale or renewal of a customer
- Help enable prospects, partners, and internal HPE teams on HPE's value in the AI landscape and how HPE and partner solutions can help solve real world business problems
Requirements:
- Bachelor's, Master's or other Advanced degree in Engineering, Computer Science, or similar quantitative focus
- 4 years + experience working with Machine Learning or Deep Learning
- Experience working with Kubernetes
- Competency working with the latest LLM frameworks, both Open Source (e.g. LangChain, LllamaIndex) and proprietary (e.g. NVIDIA NeMo/NIM)
- Competency writing ML code (for example, using PyTorch)
- Experience with Python, Unix-like systems
- Ability to quickly prototype functionality into scripts for demos, integrations, troubleshooting, etc
- Understanding of hardware requirements associated with deep learning model training or inference, and how model attributes and performance factors affect it
- Knowledge of current AI landscape, including popular models, frameworks, applications, and capabilities
- Experience working with on-premise hardware / GPU clusters
- Strong communicator, presenter and technologist evangelist
- Curiosity/interest in continuous learning to stay at the forefront of challenges which can be addressed through AI
- Artificial Intelligence Technologies
- Cross Domain Knowledge
- Data Engineering
- Data Science
- Design Thinking
- Development Fundamentals
- Full Stack Development
- IT Performance
- Machine Learning Operations
- Scalability Testing
- Security-First Mindset