Senior ML/AI Engineer

Detailed Job Description

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 detection—while 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.