ML Engineer (CV / NLP)
- Must have at least a bachelor's degree in Computer Science or similar.
- Min. 2 years of relevant work experience.
- Proficient in Basic Machine Learning concepts: algorithms, evaluation procedures, etc, and dealing with Common failure modes. Experienced in at least one area of application (E.g. CV, NLP, etc)
- Has a sound knowledge of mathematical concepts like Linear Algebra, Probability and Statistics, Calculus
- Proficient in framework & libraries such as Numpy, Pandas, Matplotlib, Scikit-learn and a good grasp of at least one of Tensorflow or Pytorch. Familiar with Flask, FastAPI or Django, and some domain-specific tools (e.g: opencv, spacy, etc)
- Good Grasp on programming language and concepts such as Python + OOP + SOLID, Data Structures and Algorithms, RESTful APIs, and familiar with Architecture Design
- Good Grasp of software tools and platforms such as git, conda, pip, jupyter, Docker, and at least one cloud platform like AWS/GCP
- Good grasp of a database such as SQL/NoSQL
- Has a good grasp of agile processes like Sprint and Kanban
- Good Team Management, Communication, and Problem-Solving Skills
- Develop AI applications to adhere to designs that support business requirements for internal and external clients.
- 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.
- Take initiative and ownership in writing requirement specifications and design documents for a variety of development tasks including feature development, database design, and system integrations.
- Preparation, drafting, and review of software documentation and project reports to meet internal and client requirements.
- Orchestrate deployment, monitoring, and maintenance of ML applications as per requirement.
- Lead one or more projects in different capacities (if required)
- Guide other developers and help them (as required) to do their work and look for ways to improve overall team output.
- Take on Leadership roles (e.g: Supervisorial) as required.
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