- Company Name
- VusionGroup
- Job Title
- Lead AI / Computer Vision Engineer
- Job Description
-
**Job title:** Lead AI / Computer Vision Engineer
**Role Summary:**
Drive the end‑to‑end development, deployment, and optimization of computer vision systems for retail applications. Lead a cross‑functional team of data scientists and ML engineers, translating research prototypes into scalable, production‑ready solutions and ensuring robust, low‑latency inference pipelines in cloud environments.
**Expectations:**
- Deliver production‑grade CV models that meet performance and reliability targets.
- Build and mentor a high‑performance engineering team.
- Maintain strong collaboration with product, research, and data teams.
- Keep abreast of emerging CV technologies and evaluate their practical adoption.
**Key Responsibilities:**
- Lead prototyping, integration, and deployment of CV models (object detection, tracking, segmentation, OCR, 3D vision, etc.).
- Architect scalable, low‑latency pipelines for image/video processing in real‑time/batch/streaming contexts.
- Optimize inference through techniques such as quantization, pruning, knowledge distillation, and deployment tools (TensorRT, OpenVINO, ONNX).
- Build and maintain data pipelines for large‑scale image/video datasets, including collection, preprocessing, augmentation, and labeling.
- Deploy models to cloud infra (AWS, GCP, Azure) and expose scalable inference APIs or streaming services.
- Implement model monitoring, drift detection, and automated retraining workflows.
- Define and enforce best practices for evaluation, benchmarking, explainability, and validation under real‑world conditions.
- Mentor team members, foster a collaborative engineering culture, and manage project timelines.
**Required Skills:**
- 7+ years in computer vision/AI engineering with 2+ years in a technical leadership role.
- Proficiency in Python and deep‑learning frameworks (PyTorch, TensorFlow).
- Expertise in modern CV techniques (detection, segmentation, classification, tracking).
- Experience deploying CV models in real‑time, batch, and streaming pipelines.
- Strong knowledge of ML‑Ops tools: Docker, Kubernetes, REST APIs, cloud automation, model lifecycle (MLflow, Kubeflow, SageMaker).
- Familiarity with performance metrics (mAP, IoU, precision/recall, latency, FPS, throughput).
- Excellent problem‑solving, architecture design, and mentoring abilities.
**Required Education & Certifications:**
- Master’s or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or related field (preferred).
- Certifications in cloud platforms (AWS, GCP, Azure) and related ML‑Ops tools are a plus.