Data Scientist – Computer Vision & AI Models

Data Scientist – Computer Vision & AI Models

We are looking for a talented Data Scientist to join our AI team and lead the research, development, and production deployment of advanced AI and Computer Vision models.

This role is ideal for someone with strong analytical and algorithmic abilities, hands-on experience with machine learning and deep learning models, and the ability to take models from research and experimentation all the way to production.

Responsibilities

• Research, develop, train, and evaluate Machine Learning and Deep Learning models.

• Work on Computer Vision tasks such as image/video analysis, detection, classification, segmentation, tracking, or related vision problems.

• Analyze structured and unstructured data, including images, videos, text, logs, documents, and APIs.

• Define success metrics, build validation methodologies, and evaluate model performance.

• Perform data exploration, feature engineering, model experimentation, and optimization.

• Build and support training, evaluation, and inference pipelines.

• Collaborate with ML Engineers, Data Engineers, Software Engineers, and Product teams.

• Support model deployment, monitoring, and continuous improvement in production environments.

• Contribute to AI solutions that may combine Computer Vision, NLP, LLMs, and multimodal models.

Requirements

• Proven experience as a Data Scientist / AI Researcher / Machine Learning Engineer.

• Strong hands-on experience developing ML/DL models.

• Experience with Computer Vision models and image or video data.

• Strong Python skills.

• Experience with PyTorch / TensorFlow / scikit-learn.

• Strong understanding of statistics, model evaluation, data quality, and performance metrics.

• Experience with data exploration, feature engineering, and model validation.

• Experience taking models from research/POC to production — significant advantage.

• Experience with SQL and databases.

• Experience with cloud platforms — AWS / GCP / Azure.

• Ability to work in a fast-paced, cross-functional environment.

Advantages

• Experience with object detection, segmentation, tracking, OCR, video analytics, or multimodal AI.

• Experience with LLMs, NLP, RAG, embeddings, or vector databases.

• Experience with MLOps, experiment tracking, model versioning, and model monitoring.

• Experience with Docker / Kubernetes.

• Experience with Spark / Kafka / large-scale data processing.

• Experience with real-time inference, edge deployment, or GPU optimization.

• Background in AI-driven or data-intensive products.