Founding Context Engineer (AI Infrastructure)

Who is Recruiting from Scratch: Recruiting from Scratch is a specialized talent firm dedicated to helping companies build exceptional teams. We partner closely with our clients to deeply understand their needs, then connect them with top-tier candidates who are not only highly skilled but also the right fit for the company’s culture and vision. Our mission is simple: place the best people in the right roles to drive long-term success for both clients and candidates.https://www.recruitingfromscratch.com/Title of the role:Founding Context Engineer (AI Infrastructure) Location: New York City (On-site)Company Stage of Funding: Pre-Seed ($3.3M), Early Product-Market FitOffice Type: OnsiteSalary: $140,000 – $220,000 + EquityCompany DescriptionWe’re representing a pre-seed startup building the infrastructure layer for modern go-to-market (GTM) systems. Their vision is to create “ambient automation” — systems that understand business context and execute intelligently without manual orchestration.They is building a universal API for B2B businesses, replacing fragmented integrations across dozens of tools with a unified Context API. Their platform enables AI systems to access, reason over, and act on structured business context reliably — solving one of the core limitations of today’s AI systems.Backed by top investors and a small, senior team from companies like Uber, Lyft, and Capchase, this company is tackling foundational problems in data infrastructure, semantic modeling, and AI-native system design. This is a ground-floor opportunity to define entirely new paradigms in how AI interacts with enterprise data.What You Will DoAs a Founding Context Engineer, you will build the core infrastructure layer that enables AI systems to reliably access and reason over business context in production.Key responsibilities include:Design and build our Context Management API, enabling structured context across workflowsDevelop new data access patterns that move beyond SQL toward semantic querying for AI systemsBuild retrieval and reasoning pipelines that understand business context before querying dataArchitect self-healing data models that automatically improve based on usage and feedbackDevelop semantic modeling infrastructure that translates business questions into precise, verifiable queriesBuild systems for identity resolution and data unification across dozens of enterprise toolsDesign knowledge graphs and context systems that evolve as business data changesWork closely with founders and customers to iterate rapidly on core infrastructure and product directionIdeal Background3+ years of experience building production systemsExperience working with LLMs, retrieval systems, embeddings, or vector databasesFamiliarity with knowledge graphs or semantic data systemsExperience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex) or multi-agent systemsStrong understanding of data infrastructure, including data warehouses (Snowflake, BigQuery) and semantic layers (dbt)Experience building or working with production ML systems (monitoring, evaluation, versioning)Strong backend engineering skills in Python or similar languagesComfortable working in fast-moving, ambiguous, zero-to-one environmentsPreferredExperience with enterprise data systems (Salesforce, Segment, HubSpot, Gong, etc.)Familiarity with workflow orchestration tools (Airflow, Prefect)Experience with graph databases or advanced knowledge graph systemsExperience building real-time data pipelines or streaming systemsExposure to multi-agent systems (LangGraph, CrewAI)Experience working with reverse ETL tools or data activation platformsCompensation and Benefits and Other ThingsBase Salary: $140K – $220KEquity: Meaningful early-stage equityWork Setup: Onsite in New York CityAdditional Highlights:Founding engineer role (engineer #5–6) with direct impact on company trajectoryWork directly with experienced founders and early customersOpportunity to define new paradigms in AI + data infrastructureHigh ownership, rapid iteration, and first-principles engineering culture