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Project description About the Role: You will lead the design, development, and deployment of real-time video analytics systems for physical security. You will combine classical computer vision pipelines with large language models (LLMs) and vision-language models (VLMs) to enable natural-language scene querying, automated incident narration, operator alert summarization, and open-vocabulary detectionwhile meeting the latency, uptime, and compliance demands of 24/7 security operations. Key Responsibilities 1. Architect multi-camera NVIDIA DeepStream (GStreamer) pipelines for concurrent 4K/HD feeds with hardware-accelerated decode. 2. Train and fine-tune detection, re-identification, and tracking models for security scenarios (low light, occlusion, fisheye). 3. Integrate VLMs (e.g., Gemini Vision, Qwen-VL) for open-vocabulary queries, scene understanding, and anomaly explanation. 4. Build LLM-powered operator workflows: natural-language alert search, incident summarization, and automated report generation. 5. Optimize CV inference with TensorRT (INT8/FP16); manage LLM inference via Triton, vLLM, or equivalent serving for latency-sensitive paths. 6. Design hybrid pipelines: fast CV models for real-time detection plus asynchronous LLM/VLM reasoning and narrative generation. 7. Integrate with VMS platforms (Milestone, Genetec, Nx Witness) and ONVIF/RTSP camera APIs. 8. Own production readiness: monitoring, model drift detection, prompt/version management, and retraining pipelines. Required Skills & Experience 1. NVIDIA & Inference Stack 2. LLM & VLM Integration 3. Computer Vision Domains (Surveillance) 4. Video Pipeline & Integration 5. Engineering & MLOps Preferred / Nice-to-Have 1. Deployments in critical infrastructure (airports, transport hubs, data centers, stadiums). 2. Experience with NVIDIA Metropolis platform or partner ecosystem. 3. Fisheye dewarping and PTZ auto-tracking. 4. Night-vision / thermal camera fusion. 5. False-alarm suppression at scale (10k+ camera networks). 6. Privacy and compliance exposure (e.g., GDPR, CCPA, NDAA 889) in video systems. 7. Knowledge of IEC 62676 and/or PSIA standards. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.