Machine Learning Engineer
Machine Learning Engineer
Posted on :
11-04-2025
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1 Vacancy
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Job Description
Machine Learning Engineers (Hands on skills on Python)
34 years of experience in machine learning in Python with a strong understanding of conversational AI natural language processing (NLP) and familiarity with OpenAIs models (Llama 2 Gemini Aphrodite).
Developing and finetuning the AI model to capture analyze calls and provide insights. Specializing in domainspecific (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 problemsolving 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 models 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 problemsolving 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 models 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.
Employment Type
Full-Time
Key Skills
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