to lead strategic AI initiatives, design scalable model architectures, and build cutting-edge visual intelligence solutions. This role demands technical depth, production-grade engineering skills, and the ability to operate at the intersection of research, enterprise AI, and real-world deployment.
Key Responsibilities
AI Strategy Leadership:
Define and drive the roadmap for Computer Vision and Deep Learning initiatives aligned with organizational and client objectives.
Model Development:
Design, train, and deploy advanced vision and multimodal models using architectures such as Vision Transformers, SAM, and multimodal LLMs.
Video Analytics & Edge AI:
Build real-time analytics solutions using
NVIDIA DeepStream
, including multi-camera pipelines, person tracking, object detection/segmentation, and smart city-scale video AI workflows.
Integration & Optimization:
Develop high-performance inference pipelines using frameworks like vLLM and
optimize models for edge, on-prem, and cloud environments
(AWS, GCP, Azure).
Perform model compression, quantization, TensorRT optimization, and GPU utilization tuning.
End-to-End ML Lifecycle:
Manage the full ML lifecycle--from data curation/annotation to training, experimentation, versioning, deployment, and monitoring in production.
Production Deployment:
Architect and deploy production-grade AI systems across
on-premise GPU servers
, edge devices, and cloud-native stacks using Docker, Triton, TorchServe, or equivalent serving frameworks.
Cross-Functional Collaboration:
Collaborate with product teams, data engineers, DevOps, and business stakeholders to translate requirements into scalable AI capabilities.
Requirements
Experience:
6-8 years of hands-on experience in Deep Learning, Computer Vision, and applied AI systems, including real-time video analytics.
Technical Proficiency:
Strong command of PyTorch, TensorFlow, OpenCV, ONNX, Hugging Face, and GPU-accelerated model development.
Video AI & DeepStream Expertise:
Hands-on experience with
NVIDIA DeepStream
, GStreamer pipelines, and GPU-accelerated video inference.
Experience in
person tracking, re-identification (ReID), multi-camera analytics
, and smart city video workflows.
Deployment Expertise:
Proven experience deploying models at scale using Docker, Kubernetes, APIs, and GPU-optimized serving frameworks such as Triton or TorchServe.
Model Optimization:
Proficiency in quantization, pruning, TensorRT optimization, batching strategies, and throughput tuning for real-time inference.
On-Prem & Edge Deployment:
Experience deploying on
on-premise GPU clusters
, edge devices (Jetson series or similar), and integrating with multiple CCTV streams.
MLOps Knowledge:
Familiarity with CI/CD for ML, data pipelines, monitoring, MLFlow/Weights & Biases, and production observability.
Soft Skills:
Strong analytical thinking, communication, problem-solving, and leadership. Passion for innovation and mentoring team members.
Why Join Us
At
Clarion Analytics
, you'll work with a forward-thinking AI team building next-generation computer vision and multimodal systems for real-world enterprise and smart city applications.
If you're passionate about production-grade AI engineering, real-time video analytics, and pushing the boundaries of intelligent vision systems, we would love to collaborate.