Customer Success Engineer

As a Customer Success Engineer, Forward Deployment Engineer or Integration Engineer for Pivotchain, you are the tip of the spear for our growth. You will regularly travel and deploy directly to customer locations to turn our cutting-edge computer vision product into a living, breathing operational reality within their environments. This is not an insulated R& D role. You own the"Technical Win. You will lead high-stakes live demos, architect and deploy fast-paced client pilots, and engineer the mission-critical plumbing required to take ravenAI from a successful proof-of-concept (PoC) to full-scale enterprise production. You are a high-agency engineer who bridges the gap between deep infrastructure engineering, real-time computer vision optimisation, and customer-facing technical leadership.

Responsibilities:

• Lead Pilots and Demos: Drive the end-to-end technical execution of ravenAI pilots on-site. Own hardware provisioning, camera stream hookups, and live configurations to prove our product's accuracy and value metrics directly to client stakeholders.
• Embedded Integration and Plumbing: Partner with client engineering teams to seamlessly integrate ravenAI into their infrastructure. Build the reliable backend APIs, data consumers, webhooks, and lightweight front-end dashboards needed to wire our vision insights into legacy enterprise workflows.
• Edge and Hardware Deployment: Install, optimise, and debug ravenAI software across unpredictable client topologies ranging from enterprise Blade servers and high-density node GPUs to local NVIDIA Jetson Orin edge devices and cloud-native environments.
• Technical Pre-Sales and Alliances: Act as a collaborative partner to our sales team. Keep your ego in check, actively listen to client objections, defuse tense political situations on the ground, and build strong engineering alliances inside the client's organisation.
• "The Keeda" (R& D Feedback Loop): Act as ravenAI's eyes and ears on the ground. Diagnose the stark gaps between clean lab environments and messy physical realities (low-quality streams, lighting shifts, occlusion). Hack together robust local workarounds under time pressure, and systematically feed structured product requests back to our core R& D team.

Requirements:

• Computer Vision Fluency: Strong understanding of CV deployment fundamentals. Experience optimising and running low-latency inference using ONNX Runtime, TensorRT, OpenCV, and PyTorch. Video Pipeline Engineering: Deep comfort building and debugging real-time video processing pipelines using GStreamer and FFmpeg. Mastery over handling high-throughput, low-latency streaming constraints (RTSP, H. 264/H. 265 ingestion).
• Edge Systems and Observability: Strong Unix/Linux environments proficiency, Python and Shell scripting, and local network diagnostics (TCP/IP, WebSockets, packet drop analysis). Expert ability to implement system logging and separate environmental "noise" from true application bugs.
• DevOps and Containers: Heavy experience packaging, deploying, and orchestrating applications using Docker and Kubernetes across both on-premises hardware and cloud environments.
• Full-Stack Prototyping: Practical understanding of full-stack plumbing, writing clean backend services/APIs, managing databases, and standing up lightweight frontends to visualise real-time vision metadata.

Soft and Architectural Traits:

• Executive-Level Presence: Ability to translate complex ML metrics (mAP, frame drop percentages, inference latency) into a crisp, 2-minute business impact summary for non-technical C-suite executives.
• Extreme Ownership & Ambiguity Alchemist: You thrive when handed "broken" client environments with zero documentation.