Forward Deployed Engineer (FDE)
Location: NJ, Onsite
The role
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. Your motto: one customer, many capabilities.
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.
What you'll do
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/Google Cloud Platform) 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.
What we're looking for
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.
Korean language ability (many stakeholders are LG affiliates) not required.
