Forward Deployed Engineer (FDE)

Job Description Position : Forward Deployed Engineer (FDE) \n Location : Englewood Cliffs, NJ \n \n [The Role] \n You are a software engineer, not a consultant. You embed directly with a customer — joining their standups, working with their data, inside their environment — and ship working AI solutions, not slides. \n Your motto: one customer, many capabilities. \n You will use our enterprise AI platform to assemble solutions in the field (data pipelines, ontology, RAG-grounded agents, applications), or our AI-SDLC framework to run AI-driven software builds — and often both. The bar you'll be held to: a working application on real customer data within the first ~10 days of an engagement. \n \n [What you'll do] \n \n Embed on-site or deeply with customer teams; map their most painful workflows with your Deployment Strategist partner and turn them into shipped software. \n Build data pipelines (e.g., PySpark) connecting customer source systems — ERP, CRM, databases, documents — with quality, lineage, and row/column-level permissions. \n Model customer data as an executable ontology (Objects · Properties · Links · Actions) and prepare it for AI consumption (vector indexes, retrieval sources). \n Build RAG-grounded agents and applications (e.g., TypeScript) that don't just answer — they act, writing back to real systems via tool-calling. \n Run fast validation loops with business users: weekly iterations, evaluation, guardrail tuning, go/no-go. \n Harden and deploy to production with the Core-Dev team — multi-cloud (AWS/Azure/GCP) or fully on-premises/air-gapped. \n 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. \n Feed what you learn back to HQ: patterns you validate in the field become standard platform components. \n \n \n [What we are looking for] \n \n Strong software engineering fundamentals — you can design, build, debug, and ship production systems end to end. \n Hands-on experience with LLM applications: RAG, agents, tool/function calling, prompt and context engineering, evaluation. \n Data engineering competence: pipelines, SQL, data modeling; PySpark or similar a plus. \n Full-stack ability to stand up usable applications quickly (TypeScript/React or similar). \n Comfort operating in ambiguity at a customer site — extracting requirements from real users, making scoping calls, and defending technical decisions to non-engineers. \n Bias for shipping: you'd rather demo something real in ten days than perfect something in three months. \n Excellent communication; you will be the face of the team at the customer. \n \n \n [Nice to have] \n \n Kubernetes/Helm/Terraform familiarity; experience deploying in restricted or air-gapped \n environments. \n Experience with AI coding agents/harnesses (Claude Code or similar) used for production-grade development. \n Ontology, knowledge-graph, or enterprise data-platform experience. \n Enterprise domain exposure: supply chain, CRM, HR systems, e-commerce, or manufacturing. \n Korean language ability (many stakeholders are LG affiliates) — not required. \n \n \n [Working style] \n \n You'll pair 2-in-1 with a Deployment Strategist (they own the "why and what," you own the "how") and rotate learnings with Core-Dev at HQ. FDE ↔ Core-Dev mobility is encouraged — field engineers who want to productize what they built are exactly who we want. \n \n