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.
- Strong 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.

Good 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.
- Strong 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