As an AI Researcher at Digital Green, you will be at the forefront of innovation, driving the development of language, voice, and computer vision models specifically tailored for the agricultural domain. Your work will directly contribute to enhancing the capabilities of our conversational bots, voice-enabled interfaces, and computer vision systems, enabling them to effectively understand and respond to user queries in local languages, recognize speech accurately, and analyze agricultural images. This role focuses on leveraging cutting-edge machine learning techniques to address key challenges faced by smallholder farmers, ultimately driving positive impact and transformation in agricultural practices and livelihoods.
Key Responsibilities:
Domain-specific Language Modeling:
- Develop domain-specific language models trained on agricultural text data to improve our bots ability to understand and generate content related to farming practices, crop management, pest control, weather forecasting, and other relevant topics. This includes enhancing comprehension and generation in local languages commonly used by farmers and designing algorithms to recognize and interpret agricultural terminology, abbreviations, and regional dialects.
Voice Recognition Model Development:
- Develop and optimize automatic speech recognition (ASR) models for native languages spoken by smallholder farmers, incorporating domain-specific terminologies and regional dialects, using advanced techniques like deep learning, transfer learning, and data augmentation to ensure high accuracy and reliability in real-world settings.
Computer Vision Model Development:
- Design and develop computer vision models and generative AI algorithms for agricultural applications, including plant disease detection, field infestation monitoring, crop detection, and image generation from textual descriptions, utilizing real data collected from agricultural fields and remote sensing platforms..
Data Collection and Annotation:
- Formulate and coordinate a comprehensive plan with program teams and third-party services to collect, annotate, and ensure high-quality agricultural text, speech, and image data in local languages, capturing diverse linguistic variations, agricultural contexts, and image scenarios for training and fine-tuning models.
Model Training, Optimization, and Evaluation:
- Train and optimize language, ASR, and computer vision models using state-of-the-art techniques to achieve high accuracy and reliability.
- Evaluate model performance using key metrics such as accuracy, word error rate, precision, recall, and F1 score, and iteratively refine models based on feedback and insights.
Collaboration and Integration:
- Collaborate closely with software engineers, data scientists, agricultural experts, and product managers to integrate AI capabilities into Digital Greens platforms, ensuring seamless functionality and user experience.
- Share insights and learnings with internal teams, government partners, and stakeholders to facilitate knowledge transfer and capacity building.
Continuous Improvement and Knowledge Sharing:
- Stay updated with the latest advancements in NLP, ASR, and computer vision research and techniques, continuously refining and optimizing models to deliver state-of-the-art performance.
- Document model architectures, training procedures, and best practices, and share knowledge with cross-functional teams and stakeholders.
Qualifications:
- Education: Ph.D. or Masters degree in Computer Science, Artificial Intelligence, Engineering, or a related field with a focus on NLP, speech recognition, or computer vision.
- Experience: Proven experience of 8+ years in AI research, with a strong track record of developing language models, ASR models, and computer vision algorithms. Experience in the agricultural domain is a plus.
- Programming Skills: Proficiency in Python and experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face Transformers, Kaldi, Mozilla DeepSpeech, OpenCV, and Keras.
- Analytical Skills: Strong analytical and problem-solving skills, with the ability to critically evaluate research papers, design experiments, and interpret results.
- Domain Knowledge: Familiarity with agricultural practices, terminology, plant biology, crop diseases, and field monitoring techniques, with a demonstrated ability to customize AI solutions for domain-specific requirements.
- Communication Skills: Excellent interpersonal and communication skills, with the ability to collaborate effectively with cross-functional teams and stakeholders and communicate technical concepts to non-technical audiences.
