Machine Learning Engineer

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Machine Learning Engineer

FigMd

Pune INR 12.00 - 17.00 LPA Experience : 6 YRS. Openings: 3

Details:

FIGmd is the only company that is directly ingesting data from over 100,000 providers with 2 PB of data every year and billions of data points. We are looking for people who believe that Machine Learning can change Healthcare.We have use cases that are likely to be disrupters in this field and need talented individual who will take the journey with us.

Technical/Domain Skills :

  • Proven experience as a Machine Learning Engineer or similar role
  • Understanding of data structures, data modeling and software architecture
  • Deep knowledge of math, probability, statistics and algorithms
  • Ability to write robust code in Python, Java and R
  • Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
  • Excellent communication skills.
  • Ability to work in a team.
  • Outstanding analytical and problem-solving skills.

Min. Qualification:

BSc in Computer Science, Mathematics or similar field. Master’s degree is a plus

Skills Required:

Team Player, Flexible, Strong written and verbal communication skills, Aggressive but level-headed to handle issues, Right Attitude and Proactive - with goal to get things done

Responsibility:

We are looking for a Machine Learning (ML) Engineer to help us create artificial intelligence products.Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and programming. If you also have knowledge of data science and software engineering, we’d like to meet you.

  • Study and transform data science prototypes
  • Design machine learning systems
  • Research and implement appropriate ML algorithms and tools
  • Develop machine learning applications according to requirements
  • Select appropriate datasets and data representation methods
  • Run machine learning tests and experiments
  • Perform statistical analysis and fine-tuning using test results
  • Train and retrain systems when necessary
  • Extend existing ML libraries and frameworks
  • Keep abreast of developments in the field

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