Key Responsibilities
Build end-to-end computer vision solutions from data preparation and model development to deployment and monitoring.
Develop and fine-tune object detection, segmentation, classification, and tracking models.
Work with PyTorch for computer vision and deep learning model development.
Evaluate models using mAP, IoU, Dice, Precision, Recall, and F1 Score.
Develop real-time video processing pipelines using OpenCV, GStreamer, and FFmpeg.
Optimize models for real-time inference using ONNX, TensorRT, and OpenVINO.
Develop and integrate REST APIs using FastAPI or Flask.
Deploy applications and ML/CV pipelines in Linux environments.
Use Docker for containerized deployments where applicable.
Debug and troubleshoot models and video processing pipelines using metrics, logs, and performance analysis.
Collaborate with backend and engineering teams to integrate CV components into production systems.
Contribute to monitoring and logging of deployed ML/CV systems.

Mandatory Skills
2+ years of hands-on experience in Computer Vision / Machine Learning Engineering.
Robust programming skills in Python.
Robust experience with PyTorch.
Hands-on experience with at least one of:
Object Detection
Image Segmentation
Object Tracking
Image Classification
Good understanding of mAP, IoU, Dice,



Precision, Recall, and F1 Score.
Strong experience with FastAPI for API development.
Experience deploying applications/models on Linux.
Experience with OpenCV.
Strong model and pipeline debugging skills.
Good problem-solving and communication skills.
Ability to work in a research-driven and hands-on engineering environment.

Positive to Have
GStreamer, FFmpeg, and RTSP streaming.
ONNX, TensorRT, and OpenVINO.
Edge deployment experience with NVIDIA Jetson, ARM, or Embedded Linux.
Experience with YOLO, Detectron2, ViT, Swin, SAM, or CLIP.
Docker and CI/CD experience.
Experience with CVAT or Label Studio.
Familiarity with MLflow or Weights & Biases.
Experience with ML/CV monitoring and logging.
Exposure to LLMs, Speech AI, or edge AI systems.

Preferred Candidate Profile
Strong hands-on experience building production-grade Computer Vision solutions.
Comfortable working across model development, APIs, video pipelines, and deployment.
Ability to optimize CV models for real-time inference and edge environments.
Robust debugging and problem-solving approach.
Comfortable working in a research-oriented environment while delivering production solutions.

📌 Computer Vision Engineer Bengaluru
🏢 Mclaren Strategic Ventures India
📍 Bengaluru