Sales Engineer at AI Squared
AI Squared is hiring a Sales Engineer in Mountain View, CA, US. On-site.
About AI Squared
AI Squared provides infrastructure for agentic AI: a platform to build, deploy, and govern AI applications across the systems that teams already use. It is built on an AI Controls Framework and connects trusted data, governed workflows, embedded delivery, and continuous feedback, with zero-trust security and enterprise-grade governance.
Sales Engineer job description
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.
Apply now
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