Pessoa Cientista de Dados Sênior

• 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.

Requirements:

• Advanced proficiency in Python and experience with major Machine Learning libraries such as Scikit-learn, TensorFlow, PyTorch, or similar technologies.


• 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.


• Experience with advanced ML/AI monitoring and observability solutions is a plus.


• Experience integrating generative AI approaches with traditional Machine Learning models is highly valued.


• Knowledge of distributed systems strategies such as replication and caching is desirable.


• Experience with Retrieval-Augmented Generation (RAG) and vector databases such as pgvector, OpenSearch, Pinecone, or FAISS is a plus.


• Familiarity with Big Data and Analytics ecosystems, including Databricks, EMR, Kafka, or Kinesis, as well as AI workload cost optimization practices, is advantageous.


• Experience developing AI agent solutions for large-scale data processing and analysis is a differentiator.

Benefits:

• Fully remote work model with the flexibility to work from anywhere.


• Flexible working hours focused on autonomy and responsibility.


• Education assistance and professional development support.


• Internal career development tools, guilds, and study groups.


• Health insurance and dental insurance coverage.


• 24/7 telemedicine access.


• Free online therapy support.


• Access to Wellhub wellness programs.


• Extended maternity and paternity leave.


• Employee support center for professional and personal assistance.


• Meal and food allowance.


• Life insurance.


• Transportation allowance.


• Home office assistance.


• Childcare assistance.


• Mobile phone allowance.


• Profit-sharing program.