Applied AI Solutions Engineer

About Solid:

Enterprises are racing to adopt AI, but AI only works if it understands complex enterprise data. Solid automatically builds and maintains a semantic layer over structured data, enabling AI systems to generate queries, support workflows, and operate with accuracy, transparency, and trust. Founded by repeat entrepreneurs, Solid is backed by leading VC funds including SignalFire and Team8.

Role Overview:

As an Applied AI Solutions Engineer, you will be the technical field lead bridging customer AI ambitions with production-ready execution. Reporting to the VP of Applied AI & Solution Engineering, you will combine technical architecture, hands-on Python development, and client-facing ownership to build semantic models, integrate workflows, and deploy Solid inside enterprise environments.

Key Responsibilities:

• Technical Delivery & Integration: Own customer deployments from kickoff to production, designing solution architectures and writing Python code for custom integrations and internal tooling.
• Semantic Modeling & AI Workflows: Build, benchmark, and certify semantic models. Connect Solid into customer AI ecosystems (MCP, Text2SQL, AI agents, Slack, internal GPTs).
• High-Touch Client Engagement: Serve as the main technical contact, leading daily communications, status syncs, ad-hoc troubleshooting, and occasional on-site visits. Work directly with BI, IT, Security, and business end-users.
• Multi-Tasking & Execution: Juggle multiple customer workstreams simultaneously, prioritizing tasks and resolving technical blockers with agility.
• Product & R&D Feedback Loop: Translate field friction, edge cases, and customer feedback into clear product requirements and Jira tickets for R&D.

Qualifications & Requirements:

• Experience: 2+ years in a technical, client-facing role (e.g., Forward Deployed Engineer, Solutions Engineer, Data Architect).
• Hands-on Development: Proficiency in Python (required for integrations, custom scripts, and tooling) and strong SQL.
• Modern Data Stack: Practical experience with cloud data warehouses (Snowflake, Databricks, BigQuery), dbt, and data modeling. Experience collaborating with BI, Data, and Security teams.
• AI Patterns: Familiarity with Text2SQL, AI agents, MCP, and workflow orchestration.
• Communication & Mindset: Excellent English skills, strong problem-solving abilities, high adaptability to multi-task in a fast-paced startup, and willingness for occasional customer visits.