AI Squared

Sales Engineer

AI Squared

Mountain View, CAPosted Dec 2, 2025
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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.