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Form of workWe are looking for a competent machine learning engineer to optimize our machine learning systems. You will be evaluating existing machine learning (ML) processes, performing statistical analysis to resolve data set problems, and enhancing the accuracy of our AI software's predictive automation capabilities. To ensure success as a machine learning engineer, you should demonstrate solid data science knowledge and experience in a related ML role. A first-class machine learning engineer will be someone whose expertise translates into the enhanced performance of predictive automation software. Roles & Responsibilities
- To research, modify, and apply data science and data analytics prototypes.
- To create and construct methods and plans for machine learning
- Employing test findings to do statistical analysis and improve models
- To search the internet for training datasets that are readily available
- ML systems and models should be trained and retrained as necessary
- To enhance and broaden current ML frameworks and libraries
- To create machine learning applications in accordance with client or customer needs
- To investigate, test, and put into practice appropriate ML tools and algorithms.
- To evaluate the application cases and problem-solving potential of ML algorithms and rank them according to success likelihood
- To better comprehend data through exploration and visualization, as well as to spot discrepancies in data distribution that might affect a model's effectiveness when used in practical situations
- Data Modelling
- Natural Language Processing - Word2vec, Sentiment Analysis, Summarisation, Gensim, and NLTK
- Unix & Linux Servers
- Programming Languages - C, C++, Python and Java
- Spark and Hadoop
- Advanced math and statistics knowledge, particularly in the areas of calculus, linear algebra, and Bayesian statistics
- Advanced degree in math, computer science, statistics, or a related field
- A master's degree in artificial intelligence, deep learning, or a related discipline
- Strong teamwork, problem-solving, and analytical skills.
- Abilities in software engineering
- Knowledge of data science
- Languages for coding and programming, such as Python, Java, C++, C, R, and JavaScript
- Practical understanding of ML frameworks
- Practical familiarity with ML libraries and packages
- Recognize software architecture, data modelling, and data structures
- Understanding of computer architecture
