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Job Location drjobs

Guatemala - Guatemala

Salary drjobs

Not Disclosed

drjobs

Salary Not Disclosed

Job Description

We are looking for an experienced and highly skilled Machine Learning Lead to join our team. The ideal candidate will have a robust background in machine learning with a particular focus on AWS SageMaker AWS Bedrock and generative AI solutions. This role involves leading a team of data scientists and machine learning engineers to develop and implement advanced ML models ensuring the delivery of highquality datadriven insights and solutions.


Who We Are:

SourceFuse is transforming the way todays most successful companies develop breakthrough roadmaps leveraging CloudBased Technologies to boost efficiency ensure compliance deliver actionable insights and lower cost of total ownership.


Key Responsibilities:

  • Lead the design development and deployment of machine learning models and algorithms to solve complex business problems.
  • Oversee the endtoend machine learning lifecycle including data preprocessing model training evaluation and deployment.
  • Utilize AWS SageMaker to build train and deploy machine learning models.
  • Leverage AWS Bedrock to develop robust and scalable generative AI solutions for various applications.
  • Develop and implement generative AI solutions to enhance business processes and customer experiences.
  • Implement MLOps practices including model monitoring and automation of the training and deployment pipeline.
  • Collaborate with crossfunctional teams to integrate ML models into production environments and applications.
  • Provide technical leadership and mentorship to a team of data scientists and machine learning engineers.
  • Communicate complex machine learning concepts and insights to nontechnical stakeholders.
  • Ensure the integrity and quality of data throughout the machine learning pipeline.
  • Stay current with the latest advancements in machine learning AI and AWS technologies to continuously improve capabilities.
  • SQL analytics integrated with generative AI solutions to deliver refined datadriven insights for informed decisionmaking and data integrity.


Requirements:

  • Minimum of 7 years of handson experience in machine learning and data science roles with significant experience in leading and managing ML projects.
  • Proven track record of successfully delivering endtoend machine learning solutions from concept to production deployment.
  • Extensive expertise in AWS services such as SageMaker AWS Bedrock and other AI/ML tools.
  • Strong proficiency in Python and familiarity with machine learning libraries such as TensorFlow PyTorch and ScikitLearn.
  • Experience with MLOps practices including model monitoring deployment automation and CI/CD pipelines.
  • Demonstrated ability to design and implement scalable and efficient machine learning architectures in cloud environments.
  • Deep understanding of data engineering feature engineering and data preprocessing techniques.
  • Experience with AutoML hyperparameter tuning and model optimization techniques.
  • Leadership skills with the ability to lead and mentor a team of data scientists and machine learning engineers effectively.

Skills: Aws Sagemaker Aws Bedrock PyTorch Tensorflow scikitlearn statsmodels pandas numpy prophet darts optuna hyperoptMLOps AutoML SQL.


Personal Traits:

  • Strong portfolio and excellent attitude.
  • Ability to work with teams across organizational boundaries different cultures and different time zones in a virtual environment
  • Delivery oriented and able to work under strict deadlines.
  • Must be selfconfident to work in a Team and to handle the responsibilities individually as well.
  • Should be a good listener/ Can articulate well / Good Communication Skills.


Whats in it for You:

  • $4000 USD monthly salary.
  • Fulltime remote work flexibility.
  • Opportunity for professional growth.


If you believe your experience and skills align with this position we would love to hear from you!

Remote Work :

No

Employment Type

Full Time

Company Industry

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