Forward Deployed Engineer

Embed on-site or deeply with customer teams; map their most painful workflows with your Deployment Strategist partner and turn them into shipped software. Build data pipelines (e.g., PySpark) connecting customer source systems - ERP, CRM, databases, documents - with quality, lineage, and row/column-level permissions. Model customer data as an executable ontology (Objects · Properties · Links · Actions) and prepare it for AI consumption (vector indexes, retrieval sources). Build RAG-grounded agents and applications (e.g., TypeScript) that don’t just answer - they act, writing back to real systems via tool-calling. Run fast validation loops with business users: weekly iterations, evaluation, guardrail tuning, go/no-go. Harden and deploy to production with the Core-Dev team - multi-cloud (AWS/Azure/GCP) or fully on-premises/air-gapped. Deliver AI-driven software builds (new applications, SaaS replacements, migrations) using our AI-development harness - orchestrating specialist AI agents through a gated lifecycle rather than hand-writing every line. Feed what you learn back to HQ: patterns you validate in the field become standard platform components.

Strong software engineering fundamentals - you can design, build, debug, and ship production systems end to end. Hands-on experience with LLM applications: RAG, agents, tool/function calling, prompt and context engineering, evaluation. Data engineering competence: pipelines, SQL, data modeling; PySpark or similar a plus. Full-stack ability to stand up usable applications quickly (TypeScript/React or similar). Comfort operating in ambiguity at a customer site - extracting requirements from real users, making scoping calls, and defending technical decisions to non-engineers. Bias for shipping: you’d rather demo something real in ten days than perfect something in three months. Excellent communication; you will be the face of the team at the customer. Nice to have Kubernetes/Helm/Terraform familiarity; experience deploying in restricted or air-gapped environments. Experience with AI coding agents/harnesses (Claude Code or similar) used for production-grade development. Ontology, knowledge-graph, or enterprise data-platform experience. Enterprise domain exposure: supply chain, CRM, HR systems, e-commerce, or manufacturing.