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AI Squared

Sales Engineer

Mountain View, CA · On-site · Sales

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About the role

AirflowAzureMachine LearningAWSGCPKubernetesCI/CDMLOpsCustomer Success
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.