Senior Software AI Engineer

Keystone AI Keystone AI is the leading cloud-native data modeling platform trusted by Fortune 500 enterprises across financial services, healthcare, retail, and technology. We help data teams design, govern, and evolve their data architecture with confidence — at scale, in real time, collaboratively. We are a profitable, growing company with enterprise customers including some of the world's largest organizations. Our platform is in the middle of a significant transformation: we are embedding AI deeply into every layer of the data modeling workflow, and we are looking for engineers who want to build what comes next. About the role This is not a role where you will bolt AI onto an existing product as an afterthought. Keystone AI is rebuilding core workflows around AI — from how data architects design schemas to how engineers validate changes to how governance teams maintain compliance across enterprise environments. You will work at the intersection of .NET backend engineering and applied AI — building the systems that make AI a first-class, reliable, enterprise-grade capability inside one of the most technically demanding categories in data infrastructure. This role sits inside our dedicated AI group and reports directly to senior leadership. You will have a short line to product, architecture, and business decisions — no layers, no queue. The team is small by design, moves fast, and has direct visibility into enterprise customer needs. What makes this role different You are not building a chatbot. You are building AI infrastructure for enterprise data teams — systems that understand schema context, generate governed artifacts, and integrate into the workflows of the world's most sophisticated engineering organizations. The problems are hard. The customers are demanding. The work is meaningful. What you'll do You will own and extend the backend systems that power Keystone AI '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 Keystone AI 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 Keystone AI 's Model Context Protocol server so that external AI agents and LLM-based tools can use Keystone AI as a trusted, real-time schema authority Tech Stack includes: C# / .NET AWS PostgreSQL Redis REST APIs LLM Integration MCP Microservices Our backend is built on modern .NET with a microservices architecture deployed on AWS. AI capabilities are built on top of major LLM providers via API, with proprietary prompt engineering, context management, and output validation layers that are core intellectual property of the platform. Qualifications 5+ years of backend engineering experience with C# and .NET Proficiency in English 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 Strong Plus 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 What We Offer Competitive base salary and equity in a profitable, growing company Fully remote — work from anywhere Direct impact on product direction — small team, no layers, your work ships to enterprise customers Work on genuinely hard technical problems at the frontier of AI and enterprise data infrastructure Collaborative, engineering-driven cultur