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 .
- Positive 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 .

Good 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