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 bridge deep backend data integration with technical execution for enterprise clients. Reporting to the VP of Applied AI & Solution Engineering, you will drive the core integration layer - building production-grade Python workflows, complex ETL pipelines, and multi-system connectors while serving as a communicative technical partner to ensure enterprise customers successfully deploy Solid. Key Responsibilities: Data Integration & Backend Development: Write production-grade Python scripts, custom connectors, and robust data pipelines to integrate Solid across complex, multi-system enterprise environments. Semantic Modeling & Validation: Build, benchmark, and certify semantic models matching customer data structures, schemas, and business logic across diverse databases and cloud data warehouses. AI & Workflow Integration: Connect Solid into customer AI agent ecosystems (MCP, Text2SQL, CrewAI, LangChain, Slack, internal GPTs). Technical Engagement & Delivery: Partner with customer technical and data teams during kickoff and deployment, serving as a solution-oriented focal point to guide technical architecture and ensure successful implementation. Field-to-Product Feedback Loop: Translate customer edge-cases, technical friction, and integration requirements into actionable specs and Jira tickets for R&D. Qualifications & Requirements: Core Software & Python Development: 5+ years of hands-on software development experience in Python, with a strong focus on clean, scalable code and backend integration. Data Engineering & Complex Architecture: Proven background integrating with multiple disparate systems, writing complex SQL queries and working deeply with relational databases and Cloud Data Warehouses (Snowflake, Databricks, BigQuery, PostgreSQL). Communication & Service Orientation: Exceptional interpersonal skills, strong service orientation, and fluent verbal and written English with the ability to communicate technical concepts clearly. Startup Mindset: Resourceful, highly organized, and comfortable operating in a fast-moving, high-growth environment. Advantages (Nice-to-Have): Direct client-facing technical experience (e.g., Forward Deployed Engineer, Solutions Engineer, Data Architect, or Technical Consultant). Experience as a Senior Data Engineer or Backend Engineer looking to move into Applied AI and customer-facing solution architecture. Practical familiarity with AI application patterns (Text2SQL, AI agents, MCP, LangChain/LangGraph) or model benchmarking. Experience in modeling data into BI systems (like Power BI, Tableau, Looker etc.)