We Love AI JobsSales Engineer
AISquaredThis Sales Engineer supports enterprise sales of AI infrastructure solutions, partnering with account executives on technical sales and customer needs. The work includes demonstrations, proofs of concept, RFPs, and post-sales deployment support.
Skills & Expertise
Key Responsibilities
Support enterprise sales through demonstrations, proofs of concept, RFP responses, and technical deep dives.
Align AI infrastructure solutions with customer needs and strategic goals.
Assist with post-sales enablement to support successful deployments and customer satisfaction.
Full 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.