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TensorOps

Junior AI/ML Engineer

Remote (Remote)$33.6k–$33.6kentryAdded yesterday

About this role

TensorOps seeks a Junior ML Engineer to develop production-grade AI and machine learning systems for clients ranging from startups to enterprises. You'll work on generative AI, traditional ML, and MLOps projects while receiving mentorship from experienced engineers in a fully remote environment.

What you'll do

  • Develop and implement machine learning models and systems across generative AI, time series forecasting, computer vision, and other domains
  • Build and maintain ML pipelines using tools like FastAPI, Docker, and Kubernetes
  • Collaborate with senior team members to design and deploy production-grade ML solutions
  • Write clean, well-tested Python code following software engineering best practices
  • Participate in end-to-end project delivery from design through deployment and monitoring
  • Contribute to MLOps improvements and observability implementations using MLFlow and similar tools

What they're looking for

  • Python programming with strong software engineering fundamentals
  • Machine learning system design and ML pipelines
  • Large language model (LLM) system design including RAG and agents
  • FastAPI for building ML APIs
  • Docker and Kubernetes containerization
  • Git version control and collaborative development
  • PyTorch, HuggingFace, LightGBM, or CatBoost model training
  • Cloud platforms (AWS or GCP)

Benefits

  • Fully remote position with legal residence in Portugal
  • Mentorship from engineers experienced in shipping ML systems at scale
  • Exposure to real-world projects with rapid feedback and measurable impact
  • Competitive compensation with growth based on ownership and performance
  • Travel expenses allowance
  • Urban Sports Club membership
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TensorOps

TensorOps builds machine learning solutions spanning generative AI applications and MLOps infrastructure. The company is hiring Junior AI/ML Engineers for remote roles where they work on diverse projects with mentorship from senior engineers.

View all jobs at TensorOps

Likely interview questions

  • Walk us through a machine learning project you've built—what was your role, what challenges did you face, and how did you overcome them?
  • How do you approach designing a system that needs to handle both model training and real-time inference at scale?
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