Machine Learning Engineer

Job Description

Machine Learning Engineers (Hands on skills on Python)
 

3-4 years of experience in machine learning in Python, with a strong understanding of conversational AI, natural language processing (NLP), and familiarity with OpenAI's models (Llama 2, Gemini, Aphrodite).
 

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.
 

• Roles and Responsibilities of a Machine Learning Engineer
   
• 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 internet for training datasets that are readily available.
   
• ML systems and models should be trained and retrained as necessary.
   
• To improve 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 situation
   
• 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.
   

Requirements

• Roles and Responsibilities of a Machine Learning Engineer
   
• 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 internet for training datasets that are readily available.
   
• ML systems and models should be trained and retrained as necessary.
   
• To improve 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 situation
   
• 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.