Data Insights Developer

1 semana atrás

São Paulo, São Paulo, Brasil beBeeMachineLearning Tempo inteiro R$116.423 - R$183.611

Unlocking Data Insights

The data science team is dedicated to extracting valuable knowledge from vast amounts of industrial data. By applying advanced statistical methods, algorithms, and data visualization techniques, the team transforms raw data into actionable intelligence that drives strategic decision-making across engineering, product development, and operational strategies.

What You'll Do

We're seeking a Mid-Level Machine Learning Engineer to bridge the gap between data science and production systems. You'll be responsible for owning end-to-end deployment of machine learning models, working with real-time sensor data, and building reliable services that power diagnostics for industrial equipment.


Key Responsibilities
  • Deploy and maintain ML models from the data science team.
  • Design and implement APIs and real-time inference services.
  • 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.
  • Contribute to architectural decisions and collaborate across teams.
Requirements
  • 2-4 years of experience in software or machine learning engineering.
  • Bachelor's degree in Computer Science, Engineering, or related technical field.
  • 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
  • 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
  • Programming: Python, Golang.
  • ML Libraries: scikit-learn, PyTorch, TensorFlow.
  • Backend: FastAPI, Flask.
  • Infrastructure: Kafka, Redis, PostgreSQL, Docker.
  • ML Ops: Model serving, monitoring, CI/CD pipelines.

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