About SuperApp

AI was single-player. SuperApp makes it multiplayer.

SuperApp is building the AI collaboration super app—a shared workspace where people and leading AI models think, create, and make decisions together in real time. Instead of splitting communication, AI assistance, and content creation across separate tools, SuperApp brings group messaging, multi-model intelligence, and collaborative creation into one continuous experience.

Teams can move from an initial conversation to a finished document, presentation, spreadsheet, or interactive app without losing the sources, decisions, feedback, and context that shaped the work. SuperApp is multi-provider by design: people can choose, pin, or compare leading models, while the platform can route each step to the capability best suited to the task. (superapp.chat)

About the role

As a Solution Engineer, you will turn customer pain points into practical AI solutions using AIHub and Superapp. You will work directly with business and technical stakeholders to understand their workflows, identify high-value use cases, and deliver solutions that achieve measurable business outcomes.

This customer-facing, hands-on role spans discovery, tailored demonstrations, pre-sales technical proofs of concept (POCs), implementation, and adoption. You will partner with Sales to win technical evaluations and collaborate with Solutions Architecture, Professional Services, Customer Success, and Product Engineering to help customers succeed.

This is not a traditional product software engineering position. It combines technical consulting and communication with hands-on Python development, prompt engineering, and customer-site collaboration.

What you’ll do

Discover customer needs: Lead workshops to understand pain points, existing workflows, technical constraints, and desired outcomes. Translate these findings into scoped use cases and solutions using AIHub, Superapp, or both.
Deliver compelling demos: Build and present tailored demonstrations that connect product capabilities to customer priorities. Explain solution architecture, value, and trade-offs clearly to technical and non-technical audiences.
Win technical POCs: Partner with Sales to define POC scope, timelines, evaluation plans, and measurable success criteria. Own technical execution, address objections, and present evidence-based results that support the customer’s buying decision.
Build custom AI solutions: Use Python, prompt engineering, APIs, and databases to develop prototypes, integrations, and customer-specific workflows. Test and refine solutions for accuracy, reliability, and maintainability.
Support implementation and adoption: Help transition successful POCs into production implementations. Troubleshoot issues, document solutions, train customer teams, and coordinate clear handoffs and ownership.
Work alongside customers on-site: Travel to customer locations as required for discovery, demos, POCs, implementation, troubleshooting, and enablement. Collaborate with customer teams to achieve agreed goals.
Learn and improve continuously: Quickly learn new technologies and product capabilities, apply them to customer problems, and turn reusable solutions and customer feedback into better delivery practices and product improvements.

About you

Industry experience of 2+ years in customer-facing technical work, such as solution engineering, technical consulting, implementation, or pre-sales, including hands-on technical evaluations or POCs.
Bachelor’s or Master’s degree in Computer Science Engineering or similar fields.
Demonstrated ability to uncover customer pain points, translate business needs into technical requirements, and propose solutions with measurable outcomes.
Strong communication, presentation, and stakeholder-management skills, including the ability to deliver engaging demos and explain complex concepts simply.
Hands-on proficiency in Python: writing, debugging, testing, and maintaining scripts, integrations, and customer-specific solutions.
Practical prompt engineering skills, including designing instructions and examples, evaluating outputs against representative test cases, and iterating to improve quality and consistency.