About the Role
We’re looking for a mid-level AI Developer to build, train, and deploy ML / DL models—especially across Computer Vision and core Python stacks. You’ll own model lifecycle end-to-end : problem framing, data pipelines, training, optimization, and production deployment.
Key Responsibilities
- Translate business problems into ML use cases; define success metrics and baselines.
- Design and implement ML / DL pipelines (data prep, feature engineering, model training, evaluation, and monitoring).
- Build Computer Vision solutions (classification, detection, segmentation, OCR, tracking, quality inspection).
- Develop robust Python services, APIs, and inference microservices; integrate with existing systems.
- Optimize models for speed, accuracy, and cost (quantization, pruning, batching, ONNX / TensorRT, GPU utilization).
- Create and maintain MLOps workflows (versioning, CI / CD, experiment tracking, automated retraining).
- Document experiments, communicate results, and present insights to technical & non-technical stakeholders.
- Ensure data security, model governance, and compliance with internal standards.
Must-Have Skills
Python (NumPy, Pandas, PyTorch and / or TensorFlow / Keras).Solid grasp of ML fundamentals (supervised / unsupervised learning, evaluation metrics, cross-validation, bias / variance).Deep Learning for CV : CNNs, transfer learning, pre-trained backbones (ResNet / EfficientNet / ViT), augmentation.Computer Vision toolchain : OpenCV, object detection (YOLO / Detectron / SSD), segmentation (U-Net / Mask R-CNN).Experience packaging inference as REST / gRPC services (FastAPI / Flask) and deploying on Docker (basic Kubernetes a plus).Hands-on with Git , experiment tracking (Weights & Biases / MLflow), and model / version management.Good-to-Have
OCR (Tesseract, EasyOCR), document AI, key-value extraction.Edge / embedded deployment (NVIDIA Jetson, TensorRT, OpenVINO).MLOps : Airflow / Prefect, Kubeflow / SageMaker, feature stores, data versioning (DVC).NLP basics (transformers) and vector databases.Cloud exposure (AWS / GCP / Azure) — GPUs, storage, IAM basics.Basic frontend / backoffice UI (Streamlit / Gradio) for demos.Qualifications
Bachelor’s / Master’s in CS, ECE, Data / AI, or equivalent practical experience.3–5 years in applied ML / AI with at least 2+ successful production deployments.