Job Title: Deployement Engineer, Computer Vision
Location: On-site at Client Deployments / Hybrid (Significant domestic travel required)
Experience: 0 - 3+ Years in Software Engineering (brownie point : if has 2+ years field/client-facing or integration engineering)
About the Role
As a Deployed Engineer (DE) for ravenAI, 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 optimization, and customer-facing technical leadership.
What You'll Own (Core Responsibilities)
● Lead Pilots & 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 & 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 & Hardware Deployment: Install, optimize, 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 & 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 organization.
● "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.
Technical Skill Requirements
● Computer Vision Fluency: Strong understanding of CV deployment fundamentals. Experience optimizing 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 & 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 & Containers: Heavy experience packaging, deploying, and orchestration applications using Docker and Kubernetes across both on-premise 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 visualize real-time vision metadata.
Soft & 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. When a delivery is due by Friday, you never say "that's not my area." You value solving the problem over proving your code was right.
Pay: ₹240,000.00 - ₹300,000.00 per year
Benefits:
- Cell phone reimbursement
- Commuter assistance
- Health insurance
- Life insurance
- Paid sick time
- Paid time off
- Provident Fund
Work Location: In person
