ML/Data Engineer

Intetics · Remoto · Remote

Recebendo candidaturas · conferida em 29/10

Ficha da vaga

Modelo
Remoto
Onde aceita
Não informado
Senioridade
Senioridade não informada
Salário
Salário não divulgado

Fonte: Workable · Ver vaga original →

  • python
  • sql
  • cloud basico
  • docker
  • ai ml products
  • analise dados
  • resolucao problemas
  • trabalho equipe

Organizei a descrição em seções com IA. O texto completo da vaga está logo abaixo.

Sobre a vaga

Intetics is seeking a skilled ML/Data Engineer to build production data infrastructure, real-time ingestion systems, and data architectures for athlete performance and sensor data.

O que você vai fazer

  • Design and build production data infrastructure for high-volume athlete performance and sensor data.
  • Build and operate real-time and near-real-time ingestion systems for streaming data.
  • Design storage and data architectures for high-volume time-series data and longitudinal athlete records.
  • Build infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency.
  • Design and implement graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes.

O que pedem

  • Language: English B2+

Descrição completa da vaga

Intetics Inc. , a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced ML/Data Engineer to join our dynamic team on a full-time basis. Customer location: USA Candidate locations: EU ONLY Working hours: 11.00-20.00 CET Language: English B2+ Estimated start date: 16.11.2026 Duration: 12 months

Project Responsibilities Design and build production data infrastructure for high-volume athlete performance and sensor data. Build and operate real-time and near-real-time ingestion systems for streaming data. Design storage and data architectures for high-volume time-series data and longitudinal athlete records. Build infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency. Design and implement graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes. Build data and ML evaluation infrastructure that helps measure model reliability, calibration, and performance across real-world cases. Design systems that maintain strong tenant-level data isolation across clubs and customers. Establish appropriate data provenance, lineage, auditability, and observability across the platform. Work with ML and AI engineers to provide reliable data foundations for model training, inference, and evaluation. Work with sport scientists and domain experts to translate complex requirements into durable production systems. Make pragmatic technology and architecture decisions as the platform evolves.

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Aceita candidatos de onde?
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Qual o regime de contratação?
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É remota?
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