Data Science Manager

HuntingCube; Dadri; On-site; INR 2,700,000 - 3,800,000; Full time

Job summary

HuntingCube in India seeks a Senior Computer Vision Architect to lead the end-to-end CV roadmap—from problem framing and data strategy to model development, edge deployment, and production monitoring across diverse industrial sectors.

You will push state-of-the-art models into production, define engineering standards for data/versioning, annotation workflows, and experiment tracking, and optimize latency on Jetson edge devices and cloud GPUs.

Qualifications

• 5–9 years of hands-on experience in computer vision, with a track record of taking models to production.
• Strong proficiency in Python; deep knowledge of OpenCV and image/video processing fundamentals.
• Demonstrated expertise across multiple CV tasks: object detection, segmentation, anomaly detection, or tracking.
• Experience with modern model families (YOLO v8/v11/v26, transformer-based detectors, SAM) and CNN backbones.

Responsibilities

• Own the end-to-end computer vision roadmap — from problem framing and data strategy through model development and production monitoring.
• Architect and build solutions for complex vision challenges in industrial settings.
• Stay at the cutting edge of CV research and evaluate new models and techniques for production value.
• Define and enforce engineering standards for the vision stack, including data versioning, annotation workflows, and experiment tracking.
• Drive inference optimization to meet latency and cost targets on edge hardware and cloud GPUs.

Skills

Computer Vision

Python

OpenCV

Tools

PyTorch

Docker

Kubernetes

TensorRT

ONNX Runtime

OpenVINO

MLflow

Job description

Job Description

• Own the end-to-end computer vision roadmap — from problem framing and data strategy through model development, edge deployment, and production monitoring — across industrial portfolio (steel, cement, pharma, paints, and beyond).
• Personally architect and build solutions for the most complex vision challenges: novel defect types, extreme class imbalance, multi-camera fusion, low-light / high-noise factory environments, and real-time inference on constrained edge hardware.
• Stay at the cutting edge of CV research and rapidly evaluate and adopt new models and techniques — YOLO26, SAM 3, Vision Transformers (DINOv2, Swin), Grounding DINO, RF-DETR, zero-shot / open-vocabulary detection (YOLO-World, CLIP) — translating papers into production value.
• Define and enforce engineering standards for the vision stack: model training pipelines, data versioning (DVC), annotation workflows (CVAT, Roboflow, Label Studio), experiment tracking (W&B, MLflow), edge export formats (TensorRT, ONNX, OpenVINO), and CI/CD for model updates.
• Drive inference optimisation — quantisation (INT8 / FP16, GPTQ), pruning, knowledge distillation, and batching strategies — to meet latency and cost targets across NVIDIA Jetson, industrial PCs, and cloud GPU instances.

Job Description

• Own the end-to-end computer vision roadmap — from problem framing and data strategy through model development, edge deployment, and production monitoring — across industrial portfolio (steel, cement, pharma, paints, and beyond).
• Personally architect and build solutions for the most complex vision challenges: novel defect types, extreme class imbalance, multi-camera fusion, low-light / high-noise factory environments, and real-time inference on constrained edge hardware.
• Stay at the cutting edge of CV research and rapidly evaluate and adopt new models and techniques — YOLO26, SAM 3, Vision Transformers (DINOv2, Swin), Grounding DINO, RF-DETR, zero-shot / open-vocabulary detection (YOLO-World, CLIP) — translating papers into production value.
• Define and enforce engineering standards for the vision stack: model training pipelines, data versioning (DVC), annotation workflows (CVAT, Roboflow, Label Studio), experiment tracking (W&B, MLflow), edge export formats (TensorRT, ONNX, OpenVINO), and CI/CD for model updates.
• Drive inference optimisation — quantisation (INT8 / FP16, GPTQ), pruning, knowledge distillation, and batching strategies — to meet latency and cost targets across NVIDIA Jetson, industrial PCs, and cloud GPU instances.

Required Skills

['Computer Vision', 'Python', 'OpenCV']

Additional Information

• 5–9 years of hands‑on experience in computer vision — with a strong track record of taking models from research / prototyping through to production deployment.
• Deep proficiency in Python and PyTorch; strong working knowledge of OpenCV, Albumentations, and image / video processing fundamentals.
• Demonstrated expertise across multiple CV tasks: object detection, instance / semantic / panoptic segmentation, anomaly detection, pose estimation, or tracking.
• Hands‑on experience with modern model families — YOLO (v8 / v11 / v26), transformer‑based detectors (RT‑DETR, DETR, RF‑DETR), segmentation models (SAM / SAM 2), and CNN backbones (ResNet, EfficientNet, ConvNeXt, Vision Transformers).
• Production experience deploying models to edge or on‑prem hardware using TensorRT, ONNX Runtime, or OpenVINO; comfort with Docker, Kubernetes, and at least one cloud platform (AWS / Azure / GCP).
• Experience in a high‑growth start‑up or similarly fast‑paced environment where scope is ambiguous, timelines are tight, and wearing multiple hats is the norm.
• Strong first‑principles problem‑solving ability — comfortable navigating novel, unstructured problems where no playbook exists.
• Excellent communication skills — able to distil complex technical concepts for non‑technical stakeholders, write clear documentation, and present results to leadership and customers.