Sales Engineer
Requirements at a glance
Sales Engineer at AI Squared is a role based in Mountain View, CA. The posting mentions 10+ years of experience. It does not mention visa sponsorship. Key skills include AWS, Azure, Google Cloud, and Kubernetes. Applications are submitted through Greenhouse.
- Experience
- 10+ years (mentioned)
- Visa sponsorship
- Not mentioned in the posting
- Application platform
- Greenhouse
Skills in this posting
- AWS
- Azure
- Google Cloud
- Kubernetes
- Machine learning
Extracted automatically from the job description. Confirm details in the full posting below.
About the Role:
We are looking for a highly motivated Sales Engineer with a strong background in AI infrastructure to join our dynamic team. In this role, you will play a critical part in driving enterprise sales, supporting both pre-sales and post-sales activities, and partnering closely with account executives to deliver cutting-edge solutions to our clients.
Key Responsibilities:
- Leverage 10+ years of experience as a Sales Engineer to drive technical sales processes and customer success.
- Sell AI-related infrastructure solutions to large and mid-sized enterprises, identifying client needs and aligning our solutions with their strategic goals.
- Partner with Account Executives to enable account-based marketing and selling (ABM/ABS) strategies.
- Operate as a self-starter, capable of working autonomously with minimal supervision in a fast-paced environment.
- Support pre-sales activities including product demonstrations, proof-of-concepts, RFP responses, and technical deep dives.
- Assist with post-sales enablement to ensure successful deployment and customer satisfaction.
- Provide deep technical knowledge of cloud-native technologies, tools, and architecture best practices.
- Demonstrate a strong understanding of AI and data pipelines, enabling clients to build scalable, intelligent solutions.
Qualifications:
- Proven experience in technical sales, ideally focused on AI, cloud, or data infrastructure.
- Strong communication and presentation skills with the ability to influence both technical and business stakeholders.
- Deep knowledge of cloud platforms (AWS, GCP, Azure), cloud-native ecosystems (Kubernetes, containers, CI/CD, etc.), and cloud-native AI tools and infrastructure—such as Amazon SageMaker, Google Vertex AI, Azure Machine Learning, Kubeflow, MLflow, and data pipeline orchestration tools like Apache Airflow and Argo Workflows.
- Familiarity with machine learning workflows, MLOps tools, and data engineering best practices.
- A proactive mindset and a customer-first attitude.