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Sales Engineer

Aisquared • MLOps • AI Governance & Compliance • 10 months ago
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Salary
≈ $146k – $281k per year (Estimated)
Location
In office (Mountain View, Menlo Park)
Seniority
Staff · 10+ years exp

Confirmed on the employer's own hiring board on Oct 11, 2026. First seen by Alion on Dec 2, 2025.

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Aisquared

Build, deploy, and govern AI inside the systems your teams already use. Zero trust security, agentic workflows, and enterprise-grade governance.
aisquared.ai • HQ: Washington, United States • MLOps • AI Governance & Compliance • 1-10 employees
Backed by NEA

About the Role: 

We are looking for a highly motivated  Sales Engineerwith 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 experienceas 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 activitiesincluding product demonstrations, proof-of-concepts, RFP responses, and technical deep dives. 
  • Assist with post-sales enablementto 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. 
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