About Biz-Tech Analytics

At Biz-Tech Analytics, we build production-grade Computer Vision and AI-driven automation solutions that transform visual data from cameras, sensors, and operational environments into actionable intelligence. Our work spans real-time video analytics, visual quality inspection, object detection and tracking, human activity understanding, and intelligent automation across diverse industrial and operational workflows.

We are looking for a Senior Machine Learning Engineer – Computer Vision who can take strong technical ownership of designing, developing, optimizing, and deploying production-scale Computer Vision systems.

Responsibilities As a Senior ML Engineer, you will work extensively on Computer Vision, image processing, and video intelligence systems, taking ownership from problem definition and data preparation through model development, optimization, deployment, and continuous improvement.

- Design, develop, and deploy advanced Computer Vision models for real-world applications including object detection, object tracking, image classification, instance/semantic segmentation, pose estimation, human activity recognition, and visual quality inspection.
- Develop real-time video analytics pipelines capable of processing high-volume image and video streams from cameras and other visual sensors.
- Work with architectures and frameworks such as YOLO, Faster R-CNN, RetinaNet, ViT-based models, SAM, Grounding DINO, and other modern vision architectures.
- Build and optimize image and video processing pipelines, including frame extraction, preprocessing, augmentation, feature extraction, post-processing, tracking, and inference.
- Develop robust solutions for multi-object tracking, person detection, behavior/activity analysis, anomaly detection, and visual event recognition.
- Perform model optimization for production inference, including quantization, pruning, batching, model compression, GPU acceleration, and inference optimization where required.
- Build Computer Vision systems for both edge and cloud environments, balancing latency, accuracy, throughput, memory utilization, and scalability.
- Work with OpenCV, PyTorch, TensorFlow, CUDA, ONNX/ONNX Runtime, TensorRT, and other relevant Computer Vision technologies.
- Design and maintain scalable computer vision data pipelines, including image/video collection, dataset preparation, annotation workflows, data validation,



and training-data quality improvement.
- Analyze model performance using appropriate Computer Vision evaluation metrics such as mAP, IoU, precision, recall, F1-score, tracking metrics, and inference latency.
- Debug and improve models that perform poorly in real-world conditions such as low-light environments, camera variations, occlusion, motion blur, changing backgrounds, and domain shifts.
- Lead the transition of Computer Vision PoCs into reliable production systems, ensuring models remain accurate and performant under real-world operating conditions.
- Mentor junior ML engineers on Computer Vision modelling, experimentation, debugging, optimization, and production deployment.
- Work directly with clients and internal stakeholders to understand visual AI problems, define technical approaches, develop PoCs, and translate them into scalable production solutions.
- Continuously evaluate emerging Computer Vision research, architectures, datasets, and foundation models and assess their applicability to production use cases.

Qualifications
- 2 years of hands-on experience specifically working on Computer Vision / Visual AI projects deployed in production.
- Strong expertise in Python and practical experience developing Computer Vision solutions using PyTorch, TensorFlow, OpenCV, or equivalent frameworks.
- Strong understanding of deep learning for Computer Vision, including CNNs, transformers, object detection, classification, segmentation, tracking, and representation learning.
- Proven experience building and deploying models for image and/or video-based applications.
- Strong hands-on experience with at least several of the following:
- Object Detection
- Multi-Object Tracking
- Image Classification
- Semantic / Instance Segmentation
- Pose Estimation
- Human Activity Recognition
- OCR / Document Vision
- Anomaly Detection
- Visual Quality Inspection
- Video Analytics
- Experience optimizing Computer Vision models for real-time or near-real-time inference.




- Understanding of GPU-based inference and experience with technologies such as CUDA, TensorRT, ONNX Runtime, or similar optimization frameworks.
- Experience working with large-scale image/video datasets, including preprocessing, augmentation, annotation, dataset curation, and quality analysis.
- Strong understanding of Computer Vision evaluation and error analysis, with the ability to diagnose model failures and improve performance.
- Experience deploying Computer Vision models to edge devices, on-premise infrastructure, or cloud environments.
- Good understanding of MLOps practices required to train, deploy, monitor, version, and maintain production Computer Vision models.
- Previous experience mentoring engineers or taking technical ownership of Computer Vision projects.
- Strong problem-solving skills and the ability to independently take a Computer Vision problem from concept → PoC → production.

Valuable to Have
- Experience with industrial Computer Vision, manufacturing automation, visual inspection, robotics, or surveillance/video analytics.
- Experience working with NVIDIA GPUs, NVIDIA Jetson, TensorRT, DeepStream, or edge AI systems.
- Experience with modern vision foundation models such as SAM/SAM 2, Grounding DINO, DINOv2, CLIP, ViTs, or similar architectures.
- Experience building vision-language or multimodal AI systems.
- Exposure to Generative AI and Vision-Language Models (VLMs).
- Experience with camera calibration, image geometry, optical flow, homograph, perspective transformation, or multi-camera systems.
- Experience handling challenging real-world visual environments involving occlusion, varying illumination, camera noise, motion blur, domain adaptation, and changing camera perspectives.
- Experience designing real-time streaming architectures for large-scale video inference.
- Exposure to AI governance, data privacy, security, and compliance requirements for visual data.

What You Will Own You will be expected to take ownership of the complete Computer Vision lifecycle:

Problem Definition → Data & Annotation → Model Development → Experimentation → Evaluation → Optimization → Deployment → Monitoring → Continuous Improvement

This is a highly hands-on role for someone who enjoys solving difficult Computer Vision problems and turning research-grade models into reliable production systems.

📌 Machine Learning Engineer – Computer Vision (Delhi)
🏢 Biz-Tech Analytics
📍 Delhi