Pessoa Cientista de Dados Sênior
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonMachine LearningData engineeringSparkNLPLLMMLOps
Requirements
- Advanced proficiency in Python and experience with major Machine Learning libraries such as Scikit-learn, TensorFlow, or PyTorch.
- Strong practical experience with Natural Language Processing (NLP) and Large Language Models (LLMs).
- Solid understanding of data and ML pipeline architecture, including training, evaluation, deployment, and inference workflows.
- Experience using cloud platforms, particularly AWS, for data processing and model deployment.
- Knowledge of modern Machine Learning Engineering and AI production practices.
- Experience working with Big Data and distributed processing technologies such as Spark, including batch and streaming pipelines.
- Hands-on experience with MLOps and/or LLMOps practices, including experiment tracking, model registries, observability, monitoring, and model drift detection.
- Ability to design scalable, reliable, and maintainable AI solutions in production environments.
- Strong problem-solving skills, analytical thinking, and ability to collaborate across technical and business teams.
Responsibilities
- Act as a technical reference in Machine Learning and Artificial Intelligence, supporting technical and product decisions with expertise in NLP and Large Language Models.
- Design, implement, and maintain data and machine learning pipelines covering model training, evaluation, deployment, and inference processes.
- Develop scalable and reliable AI solutions focused on performance, quality, and measurable business outcomes.
- Apply traditional Machine Learning techniques alongside modern AI approaches to solve complex business challenges.
- Research, propose, and implement innovative solutions involving NLP, LLMs, cloud technologies, and advanced AI architectures.
- Collaborate closely with product, engineering, and business teams to translate business challenges into effective AI solutions.
- Ensure AI and ML deliveries follow modern engineering practices, including scalability, observability, reliability, and production readiness.
- Support the evolution of AI capabilities through continuous improvement of models, processes, and technical approaches.
- Work with large-scale data environments and distributed processing solutions to enable robust AI applications.
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