AI Solution Engineer

Job Description:

As an AI Solution Engineer your role will be to architect, build, and deliver AI solutions in the form of demos and proof-of-concepts for customers during the pre-sales stage of customer engagements. These solutions will include a combination of hardware, software and services that aim to deliver an AI outcome in pursuit of a sales opportunity. Given the broad range of options available for each level of the solution stack, you will be expected to have an understanding of the most popular options and capabilities to make the appropriate recommendations based on prospects' use cases and expected outcomes. You will be working closely with HPE Sales, Architects, and Partners to solution and implement proof-of-concepts that can not only be used as prototypes during pre-sales but also expandable to production deployments post-sales.

In a typical day as an AI Solution Engineer, your responsibilities would include:

• 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.

About you - Experience you should bring to the role:

• 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.

Additional Skills: Accountability, Accountability, Action Planning, Active Learning (Inactive), Active Listening, Bias, Business, Business Development, Business Growth, Business Planning, Coaching, Commercial Acumen, Creativity, Critical Thinking, Cross-Functional Teamwork, Customer Experience Strategy, Customer Relationship Management (CRM), Data Analysis Management, Data Collection Management (Inactive), Data Controls, Design Thinking, Empathy, Follow-Through, Growth Mindset, Intellectual Curiosity (Inactive) {+ 8 more}