Computer Vision Developer Onsite (Coimbatore, India)

S27a; Coimbatore District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Dataclap builds AI data pipelines — annotation, RLHF, human-in-the-loop, and MLOps — for clients across North America and Europe. We seek a Computer Vision Developer who can stitch multiple models into working pipelines and train or fine-tune models rather than only consuming APIs.

You will design and build vision systems powering model-assisted labeling, auto-QA of annotations, and delivery pipelines for clients' datasets.

Qualifications

• 3+ years of software/ML engineering experience with at least 1–2 years in computer vision.
• Hands-on experience with multiple CV models across tasks (detection, segmentation, OCR, etc.).
• Experience building pipelines that chain models with pre/post-processing.

Responsibilities

• Design and build multi-model CV pipelines (detection, tracking, segmentation, OCR) at scale.
• Fine-tune/train custom models from open-source checkpoints to meet client accuracy targets.
• Evaluate models, benchmark architectures, and document trade-offs (accuracy, latency, cost).
• Build model-assisted labeling and auto-QA tooling to accelerate annotation/ HITL workflows.
• Handle data prep, augmentation, training, validation, error analysis, and iteration.

Skills

Python

PyTorch

OpenCV

YOLO

TensorFlow

CNN

CV pipelines

Tools

TensorRT

ONNX

MLflow

Job description

About the role

Dataclap builds AI data pipelines — annotation, RLHF, human-in-the-loop, and MLOps — for clients across North America and Europe. We're looking for a Computer Vision Developer who can go beyond running a single off-the-shelf model: someone who has stitched multiple models into working pipelines, and who has trained or fine-tuned models rather than only consuming APIs.

You'll design and build vision systems that power model-assisted labeling, automated QA of annotations, and delivery pipelines for our clients' datasets. This is a hands-on engineering role — you'll own problems end to end, from data and model selection through to deployment and monitoring.

What you'll do

• Design and build multi-model computer vision pipelines (e.g. detection tracking segmentation classification / OCR) that run reliably at scale..
• Fine-tune and train custom models from open-source checkpoints to hit client-specific accuracy and edge-case requirements..
• Evaluate and benchmark candidate models, select the right architecture for each task, and document trade-offs (accuracy, latency, cost)..
• Build model-assisted labeling and auto-QA tooling to accelerate our annotation and HITL workflows..
• Handle the full lifecycle: data preparation, augmentation, training, validation, error analysis, and iteration..
• Optimize models for inference — quantization, ONNX/TensorRT export, batching — and package them for deployment..
• Set up experiment tracking, versioning, and reproducible training runs..
• Collaborate with annotation, delivery, and DevOps teams, and communicate results clearly to non-ML stakeholders and clients..

Required qualifications

• 3+ years of total software/ML engineering experience, with at least 1–2 years working specifically in computer vision..
• Hands-on experience with multiple computer vision models across different task families — not just one. For example: object detection (YOLO family, Faster R-CNN, DETR), segmentation (SAM, Mask R-CNN, U-Net), classification (ResNet, EfficientNet, ViT), plus any of OCR, pose estimation, or object tracking (ByteTrack, DeepSORT)..
• Demonstrated experience building pipelines that chain multiple models together — feeding the output of one model into another, with proper pre/post-processing between stages..
• Proven experience training custom models or fine-tuning from open-source models, including preparing datasets, running training, and doing error analysis (please be ready to walk us through a specific example)..
• Strong Python and solid experience with PyTorch and/or TensorFlow and OpenCV..
• Comfort with the data side: dataset curation, augmentation, handling class imbalance, and evaluating with the right metrics (mAP, IoU, precision/recall, F1)..
• Ability to read a recent CV paper or model repo and get it running..

Nice to have

• Experience with vision-language / multimodal models (VLMs) or vision components for VLA / robotics training data..
• Inference optimization and edge deployment experience (ONNX, TensorRT, quantization, distillation)..
• MLOps exposure: Docker, experiment tracking (Weights & Biases, MLflow), model versioning, CI/CD for models..
• Familiarity with annotation platforms and data-labeling workflows (CVAT, Label Studio, or similar)..
• Cloud experience (AWS / GCP / Azure) for training and serving..
• Experience working with or delivering to international clients..

Who this role suits

You'll do well here if you're genuinely curious about models — the kind of person who benchmarks three architectures before picking one, who reads the eval numbers critically, and who has actually broken and fixed a training run. If your CV experience is limited to calling a single pre-trained model through an API, this role will likely stretch you beyond what it's asking for.