Machine Learning Engineers (Hands on skills on Python)
<\/p>
3 -4 years of experience in <\/span>machine learning in Python<\/b>, with a strong understanding of conversational AI, natural language processing (NLP), and familiarity with <\/span>OpenAI's models (Llama 2, Gemini, Aphrodite).<\/b>
<\/p>
<\/p>
Developing and fine -tuning the AI model to capture, analyze calls and provide insights. Specializing in domain -specific (e.g., agriculture, Mistral AI) and general use case generation and optimization.
<\/p>
- Roles and Responsibilities of a Machine Learning Engineer
<\/li><\/ul>- To research, modify, and apply data science and data analytics prototypes.
<\/li> - To create and construct methods and plans for machine learning.
<\/li> - Employing test findings to do statistical analysis and improve models.
<\/li> - To search internet for training datasets that are readily available.
<\/li> - ML systems and models should be trained and retrained as necessary.
<\/li> - To improve and broaden current ML frameworks and libraries.
<\/li> - To create machine learning applications in accordance with client or customer needs.
<\/li> - To investigate, test, and put into practice appropriate ML tools and algorithms.
<\/li> - To evaluate the application cases and problem -solving potential of ML algorithms and rank them according to success likelihood.
<\/li> - 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 situation
<\/li> - Knowledge of data science.
<\/li> - Languages for coding and programming, such as <\/span>Python<\/b>, Java, C++, C, R, and JavaScript.
<\/li>- Practical understanding of ML frameworks.
<\/li> - Practical familiarity with ML libraries and packages.
<\/li> - Recognize software architecture, data modelling, and data structures.
<\/li> - Understanding of computer architecture.
<\/li><\/ul>
<\/p>
<\/div><\/span>
Requirements<\/h3>- Roles and Responsibilities of a Machine Learning Engineer
<\/li><\/ul>- To research, modify, and apply data science and data analytics prototypes.
<\/li> - To create and construct methods and plans for machine learning.
<\/li> - Employing test findings to do statistical analysis and improve models.
<\/li> - To search internet for training datasets that are readily available.
<\/li> - ML systems and models should be trained and retrained as necessary.
<\/li> - To improve and broaden current ML frameworks and libraries.
<\/li> - To create machine learning applications in accordance with client or customer needs.
<\/li> - To investigate, test, and put into practice appropriate ML tools and algorithms.
<\/li> - To evaluate the application cases and problem -solving potential of ML algorithms and rank them according to success likelihood.
<\/li> - 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 situation
<\/li> - Knowledge of data science.
<\/li> - Languages for coding and programming, such as <\/span>Python<\/b>, Java, C++, C, R, and JavaScript.
<\/li>- Practical understanding of ML frameworks.
<\/li> - Practical familiarity with ML libraries and packages.
<\/li> - Recognize software architecture, data modelling, and data structures.
<\/li> - Understanding of computer architecture.
<\/li><\/ul>
<\/div><\/span>
