DealHub is a leading provider of sales engagement and revenue optimization solutions, empowering businesses to streamline their sales processes and drive growth. As a company that advocates a customer-first approach, we're known for our professional Success team and are hiring an AI Solution Expert.
As an AI Solution Expert, you will serve as the technical trusted advisor for our customers, bridging AI capabilities with their specific business goals and revenue workflows.
You will embed deeply with customers. Part consultant, part engineer, part strategic advisor: leading AI sessions, configuring AI-driven workflows using DealHub MCP, training users on best practices, and ensuring measurable business outcomes. You'll collaborate closely with Customer Success, Product, Support, and Engineering teams, and will be the go-to expert for any AI-related technical solution. This is an ideal role for someone who thrives at the intersection of cutting-edge AI technology and real-world business challenges.
This role is ideal for someone who thrives on ambiguity, moves fast, and finds equal satisfaction in writing clean code and presenting a solution to a CFO.
Key Responsibilities
- Embed deeply with customers to understand their technical environment, business processes, and AI workflows and then design and build solutions that fit.
- Build and deploy AI-powered workflows, MCP-based automations, and intelligent features within the DealHub platform tailored to each customer's use case taking into account the integration landscape such as Salesforce, HubSpot, Dynamics, NetSuite, and billing platforms.
- Leverage LLM, AI, APIs, webhooks, data pipelines, automation scripts.
- Lead technical AI sessions — review requirements, demo solutions, help build agents and perform training.
- Translate complex AI and technical concepts into clear, tangible business value; operate as a trusted advisor, not just an implementer.
- Identify and resolve technical blockers proactively; operate with confidence under ambiguity and incomplete information.
- Build reusable engineering assets: integration libraries, prompt templates, and AI workflow scaffolding that scale across the customer base.
- Act as a direct feedback loop from the field to the Product and Engineering teams surfacing patterns, edge cases, and AI feature opportunities discovered during deployments.
- Monitor post-go-live and drive continuous improvement.