Added
6 days ago
Type
Full time
Salary
Salary not provided

Related skills

docker python kubernetes tensorflow pytorch

πŸ“‹ Description

  • Build, maintain, and evolve scalable ML infrastructure for model development, training, evaluation
  • Design reliable, cost-effective ML pipelines, platforms, and tooling for large-scale workloads.
  • Collaborate with ML engineers and researchers to productionize models from prototypes.
  • Develop automation, observability, reproducibility, data lineage, and governance across ML
  • Evaluate and integrate new data sources, platforms, and tools to improve performance and efficiency.
  • Coordinate with software, product, and business stakeholders to deliver scalable ML solutions.

🎯 Requirements

  • Senior-level backend software engineering with strong ML understanding and interest in deeper ML
  • Experience designing and maintaining ML infrastructure for large-scale training, evaluation
  • Strong Python proficiency and modern ML tooling (deep learning frameworks, experiment tracking
  • Hands-on MLOps practices including CI/CD, model monitoring, reproducibility, data lineage
  • Experience with cloud-native tech, Kubernetes, distributed computing, and scalable ML
  • Understanding of AI concepts and practical use of AI tools, coding assistants, and LLM-based agents

🎁 Benefits

  • Opportunity to work on advanced ML, computer vision, geospatial analytics, and AI challenges.
  • Exposure to large-scale aerial imagery and property intelligence tech.
  • Work with modern cloud-native infrastructure, distributed computing, ML platforms, and AI tools.
  • Contribute to responsible AI, model governance, security, and risk practices.
  • Collaborative environment with ML engineers, researchers, software engineers, and product teams.
  • Professional growth through complex ML infrastructure challenges.
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