What are the responsibilities and job description for the Forward Deployed Engineer position at National Computer Systems?
Forward Deployed Engineer (FDE) – submit me at best possible rate.
Location: NJ, Onsite
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
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.
Location: NJ, Onsite
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/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.
Salary : $50 - $60