Sr. Associate AI/ML, NLP, Computer Vision

We are looking for a dynamic and experienced AI/ML Engineer at the Senior Associate level to contribute to the end-to-end development of intelligent solutions across diverse domains. The ideal candidate will have a strong foundation in machine learning, computer vision, and natural language processing, with proven experience in building and deploying scalable AI systems in real-world environments.

Key Responsibilities:

• Design and implement advanced AI/ML models to solve practical business problems, ensuring robustness and scalability.


• Develop computer vision applications using OpenCV and Tesseract for image processing, object detection, and OCR.


• Build and fine-tune NLP solutions for tasks such as text classification, sentiment analysis, and chatbot development.


• Leverage deep learning frameworks like TensorFlow to architect, train, and optimize neural networks for high-performance outcomes.


• Contribute to the development of Agentic AI systems, enabling autonomous and adaptive decision-making capabilities.


• Perform data preprocessing, cleansing, and augmentation to prepare large-scale structured and unstructured datasets for model training.


• Optimize and deploy AI models in production using GPU acceleration and cloud platforms (e.g., AWS, Azure, GCP).


• Collaborate with product managers, software engineers, and business stakeholders to integrate AI solutions into enterprise-grade applications.


• Continuously explore emerging trends and contribute to the organization s AI/ML innovation roadmap.

Key Requirements:

• 3+ years of hands-on experience in AI/ML development, with a strong track record of delivering production-ready models.


• Proficiency in Python and related AI/ML libraries (TensorFlow, OpenCV, Scikit-learn, etc.).


• Solid understanding of computer vision techniques and OCR using OpenCV and Tesseract.


• Expertise in Natural Language Processing, including NLP pipelines, transformers, and language models.


• Experience with cloud-based model deployment and MLOps best practices.


• Strong problem-solving mindset, attention to detail, and ability to work independently as well as in cross-functional teams.


• Familiarity with version control (Git) and CI/CD pipelines for ML deployment.