Solutions Engineer

Domino Data Lab builds software that empowers AI-driven organizations to develop and operate advanced data science solutions. The Solutions Engineer will lead technical evaluations, design proof-of-concept deployments, and collaborate with account executives to craft solution architectures for enterprise customers.

Responsibilities:

• 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. This does not necessarily need to be at a software vendor, 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