Machine Learning Lead

hace 2 semanas

Colombia JobItUs A tiempo completo

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 high-quality, data-driven insights and solutions.

Minimum Requirements:
  1. Minimum of 7 years of hands-on experience in machine learning and data science roles, with significant experience in leading and managing ML projects.
  2. Proven track record of successfully delivering end-to-end machine learning solutions from concept to production deployment.
  3. Extensive expertise in AWS services such as SageMaker, AWS Bedrock, and other AI/ML tools.
  4. Strong proficiency in Python and familiarity with machine learning libraries such as TensorFlow, PyTorch, and Scikit-Learn.
  5. Experience with MLOps practices, including model monitoring, deployment automation, and CI/CD pipelines.
  6. Demonstrated ability to design and implement scalable and efficient machine learning architectures in cloud environments.
  7. Deep understanding of data engineering, feature engineering, and data preprocessing techniques.
  8. Experience with AutoML, hyperparameter tuning, and model optimization techniques.
  9. Leadership skills with the ability to lead and mentor a team of data scientists and machine learning engineers effectively.
Key Responsibilities:
  1. Lead the design, development, and deployment of machine learning models and algorithms to solve complex business problems.
  2. Oversee the end-to-end machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
  3. Utilize AWS SageMaker to build, train, and deploy machine learning models.
  4. Leverage AWS Bedrock to develop robust and scalable generative AI solutions for various applications.
  5. Develop and implement generative AI solutions to enhance business processes and customer experiences.
  6. Implement MLOps practices, including model monitoring, and automation of the training and deployment pipeline.
  7. Collaborate with cross-functional teams to integrate ML models into production environments and applications.
  8. Provide technical leadership and mentorship to a team of data scientists and machine learning engineers.
  9. Communicate complex machine learning concepts and insights to non-technical stakeholders.
  10. Ensure the integrity and quality of data throughout the machine learning pipeline.
  11. Stay current with the latest advancements in machine learning, AI, and AWS technologies to continuously improve capabilities.
  12. Utilize SQL analytics integrated with generative AI solutions to deliver refined, data-driven insights for informed decision-making and data integrity.
Skills:

AWS SageMaker, AWS Bedrock, PyTorch, TensorFlow, Scikit-Learn, Statsmodels, Pandas, Numpy, Prophet, Darts, Optuna, Hyperopt, MLOps, AutoML, SQL.

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