Role & responsibilities

- Design and assemble detection pipelines (detection, tracking, zone and line logic, OCR, alerting) for use cases like intrusion, PPE compliance, vehicle counting and license plate recognition.

- Build test harnesses that run pipelines against recorded footage and report precision, recall and false-alarm rates per camera, so changes are judged on numbers rather than demos.

- Collect, clean and label datasets, train and fine-tune YOLO-family models, and compare them against a held-out test set before anything ships.

- Profile throughput and memory of pipelines on target edge hardware.

- Tune pipelines on live customer cameras during rollout: thresholds, zones, sampling rates, lighting and night conditions.

- Debug field issues from logs and footage, write up root causes, and turn them into regression tests.

You should have

- 2 to 4 years building computer vision systems, with at least one model or pipeline you took from data to production (or a responsible deployment-grade project).

- Strong Python, plus working knowledge of PyTorch, OpenCV and NumPy.

- Hands-on training and fine-tuning of object detection models (YOLO via Ultralytics or similar), including dataset preparation, augmentation and reading mAP, precision and recall honestly.

- Experience with multi-object tracking (ByteTrack, SORT or similar) and the logic built on top of it: counting, dwell time, line crossing.

- Familiarity with RTSP / IP camera streams and frame handling with OpenCV or FFmpeg.

- Daily use of AI coding agents, reviewing and owning every diff that gets merged.

- Gitflow.

- Comfort on Linux and the command line; basic Docker.

Desired Candidates





- Design and assemble detection pipelines (detection, tracking, zone and line logic, OCR, alerting) for use cases like intrusion, PPE compliance, vehicle counting and license plate recognition.

- Build test harnesses that run pipelines against recorded footage and report precision, recall and false-alarm rates per camera, so changes are judged on numbers rather than demos.

- Collect, clean and label datasets, train and fine-tune YOLO-family models, and compare them against a held-out test set before anything ships.

- Profile throughput and memory of pipelines on target edge hardware.

- Tune pipelines on live customer cameras during rollout: thresholds, zones, sampling rates, lighting and night conditions.

- Debug field issues from logs and footage, write up root causes, and turn them into regression tests.

You should have

- 2 to 4 years building computer vision systems, with at least one model or pipeline you took from data to production (or a serious deployment-grade project).

- Strong Python, plus working knowledge of PyTorch, OpenCV and NumPy.

- Hands-on training and fine-tuning of object detection models (YOLO via Ultralytics or similar), including dataset preparation, augmentation and reading mAP, precision and recall honestly.

- Experience with multi-object tracking (ByteTrack, SORT or similar) and the logic built on top of it: counting, dwell time, line crossing.

- Familiarity with RTSP / IP camera streams and frame handling with OpenCV or FFmpeg.

- Daily use of AI coding agents, reviewing and owning every diff that gets merged.

- Gitflow.

- Comfort on Linux and the command line; basic Docker.

📌 Computer Vision Engineer (Ahmedabad)
🏢 Adler Talent Solutions
📍 Ahmedabad