Data Science and Machine Learning Engineer Lead for Cloud Solutions
hace 1 semana
This is a challenging opportunity to lead the design, development, and deployment of machine learning models and algorithms to solve complex business problems. You will oversee the end-to-end machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
We are looking for an experienced professional with hands-on experience in machine learning and data science roles. The ideal candidate will have a strong background in AWS services such as SageMaker, Bedrock, and other AI/ML tools. They should also be proficient in Python, familiar with machine learning libraries like TensorFlow, PyTorch, and Scikit-Learn, and have experience with MLOps practices.
The role involves providing technical leadership and mentorship to a team of data scientists and machine learning engineers. The candidate should be able to communicate complex machine learning concepts and insights to non-technical stakeholders and ensure the integrity and quality of data throughout the machine learning pipeline.
About LatamCent
LATAMCENT 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 the cost of total ownership. We collaborate with cross-functional teams to integrate ML models into production environments and applications.
Key Responsibilities:
- Design, develop, and deploy 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
Requirements:
- Minimum of 7 years of hands-on experience in machine learning and data science roles
- 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, Bedrock, and other AI/ML tools
- Strong proficiency in Python and familiarity with machine learning libraries like TensorFlow, PyTorch, and Scikit-Learn
- Experience with MLOps practices, including model monitoring, deployment automation, and CI/CD pipelines
What We Offer:
- $4000 USD monthly salary
- Full-time remote work flexibility
- Opportunity for professional growth
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