Design and develop machine learning pipelines for various tasks, including classification, regression, and natural language processing (NLP)
Implement and experiment with different machine learning algorithms and techniques (e.g., supervised learning, unsupervised learning, deep learning)
Pre-process and clean large datasets for model training and evaluation
Train, evaluate, and optimize machine learning models using frameworks like TensorFlow, PyTorch, scikit-learn, and LangChain
Integrate machine learning models into production systems and monitor their performance
Design and develop machine learning pipelines for computer vision tasks, including object detection, image classification, and image segmentation
Implement and experiment with different machine learning algorithms and techniques specific to computer vision, such as convolutional neural networks (CNNs)
Collaborate effectively with data scientists, software engineers, and other stakeholders to ensure the successful development and deployment of machine learning solutions, particularly focusing on computer vision and LLM applications
Communicate complex technical concepts to non-technical audiences
Experience with cloud platforms (e.g., AWS, GCP, Azure) for deploying machine learning models
Utilize database knowledge to efficiently store, retrieve, and manage large datasets used for training and evaluating machine learning models
Building and maintaining GenAI based platform.
Building AI Agents to propose actions for Food Security/Strategic Commodity Reserves team and models.
Lead the architecture of a resilient, secure and scalable GenAI platform - Building and maintaining GenAI based platform.
Architecting and building a AI Agents module to propose actions for Food Security/Strategic Commodity Reserves team and models.
