Job description
Job Title: AI Sales Engineer/Solution Architect
Location: Remote (PST or MST)
Department: Sales Engineering
This role supports a leading AI and Agent Engineering observability and evaluation platform that empowers AI engineers to ship high-performing, reliable agents and applications. From first prototype to production scale, within a unified build, test, and run workspace. The company is a well-funded, high-growth organization serving 150+ enterprise and Fortune 500 customers across multiple industries.
About the role: The AI Sales Engineer partners closely with Account Executives to articulate platform value and differentiation throughout the sales process, driving proofs of concept from requirements gathering through close. The role blends executive-level communication with hands-on work ranging from high-level pitches to senior stakeholders to building small demos or POCs.
Responsibilities:
• Build relationships with technical stakeholders
• Lead product demonstrations of the platform
• Lead discovery to understand a prospect’s ML stack and collaborate with Sales to construct a compelling value proposition
• Handle technical objections and develop strategies across sales, engineering, and product to unblock deals
• Write educational and compelling blog posts on MLOps topics
• Collaborate to create and enhance documentation, recorded video assets, and other public-facing and internal enablement materials Knowledgeable in machine learning:
• Solid understanding of ML fundamentals (e.g., differences between linear regression and boosted trees, including tradeoffs)
• Experience training models using common libraries such as scikit-learn, HuggingFace, fastai, etc. Experience: • 5+ years in a customer-facing role (pre-sales, technical account management, consulting, or similar) • Experience working with large enterprise customers (e.g., Fortune 500) Technical proficiency: • Python • Linux / Unix Nice to have (Not Required): • Experience within a high-growth or scaling AI organization • Prior engineering experience in Data Engineering, MLOps, Kubernetes, or cloud platforms (AWS, GCP, Azure) • Datadog, Splunk or Grafana experience
Location: Remote (PST or MST)
Department: Sales Engineering
This role supports a leading AI and Agent Engineering observability and evaluation platform that empowers AI engineers to ship high-performing, reliable agents and applications. From first prototype to production scale, within a unified build, test, and run workspace. The company is a well-funded, high-growth organization serving 150+ enterprise and Fortune 500 customers across multiple industries.
About the role: The AI Sales Engineer partners closely with Account Executives to articulate platform value and differentiation throughout the sales process, driving proofs of concept from requirements gathering through close. The role blends executive-level communication with hands-on work ranging from high-level pitches to senior stakeholders to building small demos or POCs.
Responsibilities:
• Build relationships with technical stakeholders
• Lead product demonstrations of the platform
• Lead discovery to understand a prospect’s ML stack and collaborate with Sales to construct a compelling value proposition
• Handle technical objections and develop strategies across sales, engineering, and product to unblock deals
• Write educational and compelling blog posts on MLOps topics
• Collaborate to create and enhance documentation, recorded video assets, and other public-facing and internal enablement materials Knowledgeable in machine learning:
• Solid understanding of ML fundamentals (e.g., differences between linear regression and boosted trees, including tradeoffs)
• Experience training models using common libraries such as scikit-learn, HuggingFace, fastai, etc. Experience: • 5+ years in a customer-facing role (pre-sales, technical account management, consulting, or similar) • Experience working with large enterprise customers (e.g., Fortune 500) Technical proficiency: • Python • Linux / Unix Nice to have (Not Required): • Experience within a high-growth or scaling AI organization • Prior engineering experience in Data Engineering, MLOps, Kubernetes, or cloud platforms (AWS, GCP, Azure) • Datadog, Splunk or Grafana experience
