AI Solution Engineer

🕒 February 17

🏄 California, New York, +3 more states – Remote

🏄 California – Remote 🗽 New York – Remote 🌲 North Carolina – Remote 🌲 Oregon – Remote 🤠 Texas – Remote

💵 $172k - $349k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

💻 Solutions Engineer

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Hewlett Packard Enterprise

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 2015

🏢 Enterprise

🔧 Hardware

☁️ SaaS

Enterprise • Hardware • SaaS

Hewlett Packard Enterprise is a global technology leader providing innovative IT solutions to empower businesses. HPE offers a comprehensive portfolio of products and services, including the HPE GreenLake edge-to-cloud platform, which delivers a hybrid cloud experience enabling businesses to manage workloads across private and public clouds seamlessly. Additionally, the company specializes in supercomputing, networking, and storage solutions, along with AI and data analytics capabilities to drive productivity and operational efficiency. HPE is committed to helping organizations enhance their digital transformations while securing data and optimizing IT infrastructure.

📋 Description

• Architect, build, and deliver AI solutions in the form of demos and proof-of-concepts for customers during the pre-sales stage. • 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 to prospects. • Lead Proof-of-Concepts / Proof-of-Value engagements for HPE prospects. • 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.

🎯 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.

🏖️ Benefits

• Health & Wellbeing • Personal & Professional Development • Unconditional Inclusion