AI/ML Lead.-

hace 3 semanas

Madrid, España LatamCent 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.

Who We Are:

SourceFuse is transforming the way today's most successful companies develop breakthrough roadmaps leveraging Cloud-Based 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 end-to-end 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 cross-functional 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 non-technical 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, data-driven insights for informed decision-making and data integrity.


Requirements:

  • Minimum of 7 years of hands-on experience in machine learning and data science roles, with significant experience in leading and managing ML projects.
  • Candidate must be based in Latam
  • C1+ Level English
  • Proven track record of successfully delivering end-to-end 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 Scikit-Learn.
  • 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, scikit-learn, statsmodels, pandas, numpy, prophet, darts, optuna, hyperopt,MLOps, 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 self-confident to work in a Team and to handle the responsibilities individually as well.
  • Should be a good listener/ Can articulate well / Good Communication Skills.


What's in it for You:

  • $4000 USD monthly salary.
  • Full-time 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

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