AI Forward Deployed Engineer

Job Description

We’re looking for a Forward Deployed Engineer (FDE) to accelerate the application of AI in real-world environments by embedding with customers and partners to deliver high-impact deployments that drive measurable customer value.

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You’ll own end-to-end delivery of customer outcomes using our AI systems and edge platform—wielding a wide range of technologies across hardware, networking, sensors, data systems, and AI. You’ll integrate deeply into customer environments to compress adoption cycles, iterate based on real needs, and capture product enhancements that improve the platform for future deployments.

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This role operates at the edge of the platform, not inside its core. You have full authority over field deployment and configuration decisions through the platform’s controlled execution and release mechanisms.

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This is a hands-on role with 25-50% travel for someone who thrives in a high-ownership setting and wants to build and deploy the infrastructure that makes real-world AI possible.

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What You’ll Do

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• Own the design, deployment, and iteration of customer-facing solutions from initial problem definition through production rollout, adoption, and expansion.
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• Deliver measurable outcomes by traveling to customer sites 25-50% of the time and working closely with customer teams and embedded partner teams.
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• Deploy and operate our platform in real-world environments, including edge hardware, networking, connectivity, sensors, data flows, and AI inference.
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• Build integrations and tooling to connect customer systems (OT/IT), data sources, and workflows into production-grade applications.
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• Diagnose issues across the full stack (hardware ? network ? data ? application ? AI) and respond quickly to resolve deployment and production challenges.
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• Capture learnings from deployments and translate them into repeatable playbooks, scalable patterns, and product enhancements—using our AI-native workflow to direct AI tools to generate drafts, implementations, and artifacts quickly, then refine with engineering judgment.
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• Communicate clearly with technical and non-technical stakeholders and lead working sessions that drive decisions and execution.
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What Success Looks Like

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In your first 3 months, you will have:

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• Taken full ownership of at least one customer account and delivered a clear, measurable improvement in real-world customer value.
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• Built strong context on customer constraints and operational realities and used it to make sound trade-offs across product, systems, and delivery.
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• Earned trust through autonomy, responsiveness, and high-quality execution—becoming the person teams rely on to move deployments forward.
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In your first year, you will be:

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• Independently owning multiple deployments or a strategic account end-to-end, consistently compressing adoption cycles and driving measurable outcomes.
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• Creating repeatable deployment patterns that materially reduce delivery friction and improve speed-to-value across customers and partners.
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• Feeding a steady stream of product improvements back into Engineering, helping evolve the platform based on real-world constraints and learnings.
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Who You Are

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• 5+ years building and operating production software or systems, ideally in customer-facing or delivery-oriented roles.
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• Experience operating in complex environments across infrastructure, networking, security, data systems, and/or production AI (e.g., Linux, Docker/Kubernetes, REST/gRPC APIs, and modern observability tooling).
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• Strong engineering craft: clean implementations, thoughtful designs, operational clarity, and strong documentation (e.g., Python and/or Go, shell scripting, and reliable integration practices).
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• Comfort working in ambiguity and making sound trade-offs, especially when timelines and constraints are real.
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• Clear communicator and strong collaborator across technical and non-technical stakeholders.
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• Ownership mindset: outcomes over tasks; you naturally take responsibility for delivery, adoption, and customer value.
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Unique Experiences We Value

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• Delivering complex deployments end-to-end and turning one-off wins into repeatable patterns (e.g., deployment playbooks, runbooks, and reusable configuration templates).
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• Hands-on experience with edge or hybrid systems across hardware, networking, connectivity, and containerized software environments (e.g., VPNs, TLS, firewalls, LTE/5G/Wi-Fi, and Kubernetes deployments).
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• Deep comfort diagnosing and solving cross-stack issues in production environments (e.g., logs/metrics/traces, packet capture, network debugging, and performance profiling).
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• Production experience applying modern AI systems (LLMs, agents, inference) in real customer workflows under real constraints (latency, bandwidth, reliability, and security).
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• Experience embedding with customer and partner engineering teams to align multi-party delivery toward measurable outcomes.
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