Key Responsibilities:

Develop and deploy sophisticated computer vision models for tasks including object detection, image

classification, segmentation, OCR, and real-time video analytics.

Manage large-scale datasets by overseeing collection, annotation strategies, cleaning, and data augmentation

to ensure high-quality model training.

Architect and optimize deep learning frameworks such as CNNs, Vision Transformers, GANs, and YOLO for production-ready environments.

Collaborate with cross-functional engineering, product, and MLOps teams to integrate vision models into end- to-end production pipelines.

Enhance model performance and scalability through quantization, pruning, and conversion for edge

deployment using ONNX or TensorRT.

Execute rigorous experiments and statistical analyses, including A/B testing, to validate model accuracy and assess business outcomes.

Keep abreast of state-of-the-art research in computer vision and deep learning to identify and implement innovative solutions for business problems.

Mentor junior scientists and establish best practices for robust model development, evaluation, and

documentation.

Partner with data engineering to build high-performance, scalable data pipelines tailored for vision workloads.





Required Skills & Qualifications

7+ years of professional experience in Machine Learning with a specialization in Computer Vision.

Advanced degree (Master's or Ph.D. preferred) in Computer Science, Electrical Engineering, or a related quantitative field.

Expert proficiency in Python and frameworks like PyTorch, Tensor Flow, or Keras.

Hands-on experience with CV libraries such as OpenCV, YOLO, Detectron2, and MMDetection.

Robust background in CNNs, Vision Transformers (ViT), and image preprocessing techniques.

Proven experience deploying models via Docker, Kubernetes, FastAPI, ONNX, and TensorRT.

Skilled in cloud AI/ML services across AWS, GCP, or Azure (e.g., Sage Maker, Vertex AI).

Deep knowledge of data structures, algorithms, and software engineering best practices.

Experience managing large datasets with SQL, Spark, Pandas, or Dask.

History of taking ML models from research/prototypes into scalable production environments.

Excellent communication, problem-solving, and cross-functional collaboration skills.

Familiarity with MLOps, CI/CD pipelines, and edge AI hardware (NVIDIA Jetson, mobile inference).

📌 Senior Data Scientist (India)
🏢 CIEL HR
📍 India