Forward Deployment Engineer

As a Forward Deployment Engineer, you sit at the intersection of software engineering and customer impact. You build and deploy AI-powered integration and agentic workflow solutions directly with enterprise customers — from the first technical discovery conversation through post-launch enhancements. You are the engineer in the room who hears a customer problem and builds the solution. Speed matters here — the ability to write optimized, production-grade code fast and deploy it to customers without long feedback cycles is what makes the FDE function valuable. Strong data structures and algorithms fundamentals and an innovative, non-obvious approach to problem solving are core to what we look for. AI fluency — building agentic workflows and using LLMs as a daily engineering tool — is a baseline expectation for everyone on this team, not a specialization.

What you'll be doing:

• Build and deploy integration solutions — EDI (214, 315), callbacks, custom payload mapping, and API integrations — with production-grade quality and full test coverage
• Deploy agentic AI workflows that orchestrate supply chain operations live with customers present — configuring and deploying in the call, not gathering requirements to build later
• Write and extend LangGraph-based workflow extensions and wrapped action customizations in Agent Studio
• Use Claude, OpenAI, and other LLMs across the entire workflow — requirements documentation, integration design, code generation, testing and deployment; LLMs are not a convenience tool, they are how this team works
• Participate in customer discovery and technical clarification calls — answering complex technical and product questions with concrete, innovative solutions in real time
• Write thorough root cause analyses for integration failures and own the fix through to resolution
• Perform code reviews on integration and AI workflow work and maintain the team's quality standards before any customer deployment
• Contribute to the team's reusable integration package and AI workflow template library as you build
• Mentor incoming FDEs — pair on hard problems, accelerate their ramp-up, and share institutional knowledge
• Collaborate with Implementation Managers, Customer Success Managers, Solutions Consultants, Product Managers, and Engineering to deliver seamless customer outcomes

Who you are:

• 0–4 years of software engineering experience — what matters more than tenure is the depth and quality of what you have built
• Strong proficiency in Java (required) — writes clean, optimized, production-grade code with proper error handling, test coverage, and clear design thinking from day one
• Strong foundations in data structures and algorithms — able to reason about time and space complexity, choose the right data structures for the problem, and write code that performs at scale without being told how
• Strong software design instincts — designs integration solutions that are modular, maintainable, and built to evolve; doesn't just write code that works today, writes code that holds up over time
• Writes code at speed without sacrificing quality — can take a customer requirement from problem statement to working, deployed solution faster than traditional development cycles allow; this is a core expectation, not a nice-to-have
• Innovative problem solver — approaches integration and AI workflow challenges with genuine curiosity, finds non-obvious solutions, and doesn't default to the first approach that comes to mind
• Proficient in REST APIs, HTTP/HTTPS, SFTP, and core data formats — XML, EDI, JSON, flat files — can read, write, and transform all of them
• Working knowledge of enterprise integration patterns and how to apply them in distributed, multi-tenant systems
• Solid working understanding of LLMs, prompt engineering, and agentic workflow design — hands-on, not theoretical
• Uses Claude, OpenAI, or equivalent LLMs across the full workflow — requirements documentation, integration design, code generation, debugging, testing and deployment; not just as a coding assistant but as a core engineering tool
• Familiar with agentic frameworks such as LangGraph or LangChain and has built or configured at least one AI workflow in production or a meaningful side project
• Understand authentication patterns: OAuth 2.0, API keys, basic auth, token lifecycle management
• Comfortable on customer calls — communicates technical concepts clearly to both technical and non-technical stakeholders
• Able to deploy agentic workflows live with customers present, handling configuration and troubleshooting in the moment

Preferred Qualifications

• Experience in supply chain, logistics, transportation, or B2B SaaS platforms is a strong plus
• Familiarity with enterprise integration patterns and the ability to select the right approach for a given business and technical scenario
• Familiarity with EDI standards, carrier integrations, TMS/WMS systems, and supply chain data flows
• SQL proficiency — Snowflake experience is a plus
• Experience with low-code/no-code workflow automation platforms such as Pipedream, n8n, Temporal, or equivalent
• Proficient with Lucidchart, Miro, draw.io, or similar tools to document and communicate integration designs visually — the ability to make a complex data flow readable to a customer or a non-technical stakeholder matters as much as the build itself
• Experience working with enterprise customers in a post-sales, implementation, or technical account role
• Familiarity with webhook and callback delivery architecture