Artificial Intelligence Developer

1 semana atrás

São Paulo, São Paulo, Brasil beBeeDataScientist Tempo inteiro R$90.000 - R$120.000
Job Overview

The data science team focuses on extracting valuable insights from industrial data, utilizing advanced statistical methods and algorithms to transform raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies.

This role involves deploying and maintaining machine learning models, designing and implementing APIs and real-time inference services, working with large-scale time-series datasets, and improving the performance and reliability of model serving pipelines.

Key Responsibilities:

  • Deploy and maintain ML models
  • Design and implement APIs for real-time inference
  • Work with large-scale time-series datasets from vibration and sensor systems
  • Improve the performance and reliability of model serving pipelines
  • Monitor system health and implement logging, alerting, and fallback mechanisms
  • Collaborate across teams and contribute to architectural decisions
Requirements

To succeed in this role, you will need:

  • 2–4 years of experience in software or machine learning engineering
  • A bachelor's degree in Computer Science, Engineering, or a related technical field
  • A solid background in math, statistics, and machine learning concepts
  • Strong Python skills and experience with ML libraries like scikit-learn or PyTorch
  • Experience deploying models in production environments
  • Familiarity with event-driven platforms and message queues (e.g., Kafka, Redis Streams)
  • Comfort working with streaming or time-series data
Preferred Qualifications

Candidates with these qualifications will have a competitive edge:

  • Experience with containerization (Docker) and cloud deployment
  • Exposure to real-time or low-latency systems
  • Interest in optimization of inference latency and resource usage
Technical Skills

You will be expected to possess:

  • Programming skills in Python and Golang
  • Knowledge of ML libraries: scikit-learn, PyTorch, TensorFlow
  • Backend experience: FastAPI, Flask
  • Infrastructure knowledge: Kafka, Redis, PostgreSQL, Docker
  • ML Ops expertise: Model serving, monitoring, CI/CD pipelines

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