As a Machine Learning Engineer specializing in Natural Language Processing (NLP) for farmer.chat at Digital Green, you will have the opportunity to apply cutting-edge machine learning techniques to empower small-holder farmers with intelligent conversational bots. Working closely with cross-functional teams and government partners, you will play a vital role in developing and deploying NLP solutions tailored for the agricultural domain, ultimately driving positive impact and transformation in farming communities worldwide.
Key Responsibilities: - Model development: develop ML models and algorithms for NLP tasks such as query understanding and decomposition, query reformulation, semantic similarity matching
- Data preprocessing: Preprocess and analyze agricultural multi-modal data (text, voice, videos, etc) collected from diverse sources, ensuring data quality, relevance, and appropriateness for model training
- Model optimization: Optimize language models using state-of-the-art techniques and methodologies, and fine-tune them to perform effectively in low-resource settings and local languages.
- Model deployment and integration: Deploy trained models into staging and production environments, integrate them with our conversational bot platform
- Performance monitoring and evaluation: Monitor model performance in real-world deployments, track key metrics such as accuracy, latency, drift, readability scores etc
- Stakeholder engagement: Collaborate with government partners, product team and engineering team to understand their needs and requirements
Qualifications:
- Education: Bachelors, Masters, or Ph.D. degree in Computer Science, Engineering, or a related field with a focus on machine learning and NLP
- Experience: Proven experience of 5+ years as a machine learning engineer, data scientist, or software engineer, with hands-on experience in deploying machine learning models
- Programming Skills: Proficiency in Python and experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, and NLTK
- NLP Knowledge: Strong understanding of NLP techniques including language modeling, text classification, named entity recognition, sentiment analysis, and machine translation
- Software Engineering Skills: Solid software engineering skills, including proficiency in github/ gitlab, docker, and cloud computing platforms (eg, AWS, GCP, Azure)
- Problem-solving Skills: Excellent analytical and problem-solving skills, with the ability to understand complex requirements and devise innovative solutions
- Team Player: Strong interpersonal and communication skills, with the ability to work effectively in a collaborative team environment and contribute to a culture of innovation and excellence
