Forward Deployed Engineer
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LoadsmartLogistics Technology
US Remote with travel up to 50% to client engagements and Loadsmart's Chicago office.Full-TimeSenior
Salary115,700 - 180,800 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonTypeScriptRESTful APIs
Requirements
- 5+ years of full-stack engineering experience, with strong proficiency in Python and TypeScript, including supporting libraries and frameworks that leverage fast prototyping.
- Hands-on experience designing and implementing automated workflows and AI-driven solutions to optimize business processes.
- Proven experience building and maintaining APIs and integrations with third-party or enterprise systems.
- Demonstrated ability to work directly with non-technical stakeholders — translating operational needs into technical solutions, and clearly explaining technical trade-offs and progress, both in writing and in conversation.
- Comfortable being the person in the room when things don't go as planned — able to debug, re-scope, or improvise a solution in front of a customer rather than escalating and waiting.
- A track record of shipping working software quickly in ambiguous, fast-changing environments.
Responsibilities
- Shadow client teams and their day-to-day operations to see workflows firsthand, then run structured discovery to identify and prioritize the pain points worth solving.
- Design the right solution for each prioritized pain point - whether that's an automation, an AI agent, a system integration, or a combination - and scope it before building.
- Build and integrate that solution against real systems, including transportation management systems (TMS), warehouse management systems (WMS), and enterprise resource planning (ERP) platforms.
- Prototype and ship working automation - such as automated check calls and shipment tracking, carrier qualification and onboarding flows, and voice or chat-based agents - prioritizing action and execution speed to deliver value.
- Tune prompts, agent behavior, and voice or transcription models based on real production data and edge cases - and keep refining after launch, since agent behavior surfaces new edge cases as real usage grows.
- Identify security, compliance, and infrastructure requirements early in each project, and work with the right internal stakeholders to resolve them before they become blockers.
- Feed what you learn back to product and supporting engineering teams, turning recurring patterns into reusable platform capabilities rather than one-off fixes.
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