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 OpenAIs 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\xe2\x80\x99s 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.

• 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\xe2\x80\x99s 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.

Vsynergize OutSourcing Pvt. Ltd