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DominoDominoUnited States

Enterprise Solutions Engineer

Pre-sales technical role leading evaluations, POCs, and solution architecture for Domino's AI/ML platform with enterprise customers. Requires proven pre-sales engineering experience, Python/R/ML proficiency, and knowledge of containers, cloud, and complex IT environments.

200k – 250k/yr
Remote5+ YOESales Engineering

About the role

Impact

  • Lead technical evaluations and demonstrations of the Domino platform for Enterprise customers in a multitude of industry verticals
  • Design and execute proof-of-concept deployments tailored to each customer’s environment and mission needs, showcasing Domino’s integration with their data science workflows and infrastructure
  • Collaborate with account executives to craft solution architectures that meet industry-specific security and compliance standards
  • Develop and maintain reusable demonstration environments and technical assets that accelerate future sales cycles
  • Drive post-POC adoption readiness by partnering with Customer Success and Solutions Architects to ensure a smooth handoff into deployment

Success will be evident through higher technical win rates, reduced time to close, and increased adoption within key prospect accounts

Requirements

  • Proven success in pre-sales or solutions engineering, ideally supporting enterprise software or AI/ML platforms (equivalent solution engineering tasks internally or as a consultant developer could work)
  • Experience with Enterprise customers, understanding their procurement processes, compliance constraints, and security environments
  • Track record of leading successful technical evaluations or pilots that resulted in multimillion-dollar enterprise software deals
  • Experience working in complex IT environments, including hybrid or air-gapped systems
  • Proficiency in Python, R, and modern data science / machine learning tools
  • Familiarity with containerization (Docker, Kubernetes), cloud platforms (AWS, Azure, GCP), and networking concepts
  • Understanding of the end-to-end AI lifecycle, from experimentation to production

Skills

PythonRDockerKubernetesAWSAzureGCPMachine Learning
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