Machine Learning Engineer
About the job
At nurture.farm, we are putting the farmer at the center of everything we do. We are building a more resilient and sustainable farming ecosystem by reducing risks and improving the quality of life for the farmers and their families through digitization of the farming lifecycle, financial inclusion of the farming community, and affordable timely access to products, technologies, advisory and services.
We are a customer-obsessed organization, that enables employees to create products and services that transform the lives of farmers. We are a technology-led organization, anchored on leveraging technology to solve scalable and sustainable solutions for the farming ecosystem. We are a young entrepreneurial startup that wants to learn, create and adapt every day. We aspire to create a happy and productive workplace for our employees, that embodies respect and transparency in every part of the organization.
What we’re looking for in you
- BTech Computer Science, or similar field of study, or equivalent practical experience.
- Software development experience in one or more general purpose programming languages.
- Experience working with the following: Machine Learning Frameworks (Tensorflow, PyTorch, etc), Data Science toolkits
- Conversant with Model Training, Feature Engineering, setting up training pipelines as well as bringing models into production
- Familiarity with real time streaming, distributed computing
- Working proficiency and communication skills in verbal and written English.
- Master’s degree, further education or experience in AI/ML, computer science or other technical related field.
- Understanding of agriTech domain and application of technology in farming
- Interest and ability to learn other coding languages as needed.
- Understand the domain and come up with concrete problem definitions based on observations from the field.
- Design, develop, test, deploy, maintain and improve ML models.
- Manage individual project priorities, deadlines and deliverables.
- Enthusiastic to take on problems across the full-stack.
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