Website:
craftifai.com
Job details:
Experience: 2–5 Years
Location: Bengaluru /
Employment Type: Full-time
About the Role
CraftifAI is building a GenAI-powered, multimodal LLM-enabled, AI-native Video Management System tightly integrated with PipeGen, our Edge AI pipeline generation platform.
We are looking for a hands-on VMS & Edge AI Engineer who has experience working with video management systems, edge-to-cloud integration, AI video analytics, dashboards, and cloud/edge deployment.
The role involves building and deploying intelligent video workflows that connect cameras, edge devices, AI models, cloud systems, dashboards, and GenAI-powered interfaces.
Key Responsibilities
Design, develop, and integrate components of an AI-native Video Management System.
Work with camera streams, RTSP, ONVIF, live video, playback, event recording, alerts, and video metadata.
Integrate AI analytics pipelines for detection, tracking, classification, event generation, and real-time monitoring.
Build edge-to-cloud workflows for device connectivity, video analytics, event sync, deployment, and monitoring.
Deploy VMS and AI analytics systems across cloud, edge servers, and AI-enabled devices.
Develop or integrate dashboards for live feeds, AI events, device health, alerts, analytics, and system status.
Debug real-world deployment issues related to video streaming, latency, networking, storage, edge compute, and cloud connectivity.
Collaborate with the PipeGen team to integrate generated Edge AI pipelines into the VMS platform.
Required Skills
2–5 years of relevant engineering experience.
Hands-on experience with Video Management Systems, video surveillance platforms, camera systems, or video analytics solutions.
Strong understanding of RTSP, ONVIF, video ingestion, live streaming, recording, playback, and event-based video workflows.
Experience in edge-to-cloud integration for cameras, IoT devices, or AI systems.
Experience integrating AI analytics outputs such as bounding boxes, events, alerts, metadata, and dashboards.
Backend development experience using Python, Node.js, FastAPI, Flask, Express, or similar frameworks.
Experience with Linux, Docker, APIs, databases, and deployment workflows.
Good understanding of networking, cloud connectivity, device management, and production debugging.
Good to Have
Experience with NVIDIA Jetson, edge AI boxes, IP cameras, NVR/DVR systems, or industrial camera deployments.
Experience with GStreamer, FFmpeg, OpenCV, DeepStream, WebRTC, MediaMTX, or similar video frameworks.
Exposure to AI models such as YOLO, RT-DETR, ByteTrack, DeepSORT, segmentation, or anomaly detection models.
Experience with cloud platforms such as AWS, Azure, GCP, or Cloudflare.
Exposure to GenAI, LLM agents, multimodal AI, or AI copilots.
Experience with Edge AI, embedded systems, or FPGA-based AI pipelines is a plus.
Ideal Candidate
The ideal candidate has worked on real-world video systems and understands the practical challenges of camera streaming, video analytics, edge deployment, network reliability, cloud integration, dashboards, and production debugging.
You should be comfortable working across backend, video pipelines, AI integration, edge deployment, and system-level troubleshooting.
Why Join CraftifAI
Work on a next-generation AI-native VMS platform.
Build products combining Edge AI, video analytics, GenAI, and multimodal LLMs.
Solve real-world problems in video intelligence, edge deployment, and AI automation.
Be part of a product-focused team building the future of AI-native embedded and video systems.
Click on Apply to know more.