Senior Software AI Engineer

SqlDBM is the leading cloud-native data modeling platform trusted by Fortune 500 enterprises across various sectors. They are seeking a Senior Software AI Engineer to work on backend systems that integrate AI into data modeling workflows, focusing on building robust and reliable AI capabilities for enterprise data teams.

Responsibilities:

• You will own and extend the backend systems that power SqlDBM's AI capabilities
• Specific areas include:
• AI-assisted data modeling workflows — backend services that allow users to describe their data needs in natural language and receive accurate, governed schema output
• Context-aware intelligence — systems that use the full richness of a user's data model, naming standards, and governance rules to produce AI output that is specific to their environment, not generic
• Automated documentation and metadata generation — AI pipelines that analyze existing schemas and produce accurate, consistent business documentation at scale
• Integration with enterprise AI ecosystems — API layers that allow external AI agents and tools to call SqlDBM as a trusted source of schema context
• Consumption tracking and orchestration — backend infrastructure that manages AI request routing, model selection, cost optimization, and usage metering
• Governance-aware AI workflows — systems that embed approval, validation, and compliance logic into AI-generated outputs before they reach production
• MCP server development — building and extending SqlDBM's Model Context Protocol server so that external AI agents and LLM-based tools can use SqlDBM as a trusted, real-time schema authority

Requirements:

• 5+ years of backend engineering experience with C# and .NET
• Strong understanding of REST API design and asynchronous service architectures
• Experience integrating with external APIs and managing complex data pipelines
• Comfort working with LLMs via API — understanding of prompt construction, context management, token economics, and output validation
• Experience building systems that handle variable, structured data — schemas, metadata, or similar
• Strong engineering fundamentals — testing, code review, system design, observability
• Ability to work independently in a remote-first, fast-moving engineering team
• Experience with data modeling, database design, or data engineering tooling
• Familiarity with enterprise data platforms — Snowflake, Databricks, dbt, or similar
• Background building developer tools or platforms used by technical teams
• Experience with agentic AI workflows, tool-use patterns, or AI infrastructure
• Knowledge of semantic layer concepts, ontologies, or structured metadata systems
• Familiarity with Model Context Protocol (MCP) — building or consuming MCP servers in agentic AI architectures