Machine Learning Engineer Associate - Nepal

About the Company

Fusemachines (http://www.fusemachines.com)builds AI Schools in underserved communities (AI Education Solution) and connects the graduates to AI job opportunities (AI Talent Solution). Fusemachines AI Schools run AI Microdegree® and Certificate programs in physical classrooms using it’s proprietary content and learning platform.

 

 Qualifications & Experience:

  • Bachelor’s Degree in Information Technology
  • Must have done ML project

 
Required Skills:

  • Good Theoretical Background in Machine Learning
  • Good grasp of Basic Machine Learning concepts: algorithms, evaluation procedures, etc and mathematical foundation for ML: Linear Algebra, Probability and Statistics, Calculus, etc.
  • Proficiency in Python, OOP and Data Structures
  • Good grasp of data science libraries like numpy, pandas, matplotlib, scikit-learn, etc, deep learning libraries such as TensorFlow, Keras, Pytorch, etc and GPU computing.
  • Familiarity with domain-specific libraries: Eg: OpenCV, NLTK, etc.
  • Ability to apply AI in one or more of the following areas: Natural Language Processing, computer vision, recommendation systems, forecasting, or similar.
  • Strong written and verbal communication skills in English.
  • Creative Presentation Skill
  • Ability to conduct an independent literature review for projects and summarise the contents
  • Familiarity or the ability to learn and adapt to current trends and best practices.

 
Roles and Responsibilities:

  • Assist with writing requirement specifications and design documents for a variety of development tasks including feature development, database design and system integrations.
  • Assist with developing applications and code software applications to adhere to designs that support business requirements for internal and external clients.
  • Work under tight deadlines to deliver quality, robust software.
  • Preparation, drafting, and review of software documentation and project reports to meet senior staff and client requirements.
  • Participate in implementing new software features and maintain existing features.
  • Refactoring, debugging, testing and implementing changes to existing applications to meet project requirements.
  • Research and develop machine learning models and work on the whole ML pipeline: data collection, wrangling, pre-processing, model building, evaluation, and deployment.
  • Perform data analysis to uncover insights that can be immediately actionable or can inform decisions around the ML process.
  • Demonstrate end-to-end holistic understanding of the applications being created.
  • Help AI product managers and business stakeholders understand the potential and limitations of AI when planning and executing projects.
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