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TensorOps

Mid-Level AI Engineer

Remote (Remote)midAdded today

About this role

TensorOps seeks a Mid-Level ML Engineer to design and deploy production-grade AI systems for enterprise clients, including RAG pipelines and agentic workflows. You'll own technical delivery end-to-end while mentoring junior engineers and shaping internal best practices on a fully remote, high-impact team.

What you'll do

  • Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings)
  • Own technical delivery end-to-end from architecture and prototyping through deployment, monitoring, and iteration
  • Translate client business requirements into scoped, shippable technical solutions in collaboration with engineering and product teams
  • Mentor junior ML engineers through code reviews, technical guidance, and knowledge sharing
  • Help establish internal best practices, tooling, and technical standards as the team scales
  • Represent TensorOps in client technical conversations and industry engagements

What they're looking for

  • Python and production-quality code writing
  • ML model design, training, optimization, and deployment (PyTorch, TensorFlow, Scikit-learn)
  • GenAI and LLM systems (RAG pipelines, chatbot architectures, LangChain)
  • MLOps and production ML practices (model versioning, monitoring, CI/CD)
  • Cloud deployment and scaling on AWS, GCP, or Azure
  • Performance optimization and debugging
  • Stakeholder and client communication
  • System architecture and technical mentorship

Benefits

  • 100% remote work with no mandatory office days
  • Fully funded AWS and GCP professional certifications
  • Work on cutting-edge ML and GenAI projects across diverse industries
  • Collaboration with Fortune 500 companies and unicorn clients
  • Urban Sports Club membership
  • Monthly Bolt ride credits
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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

  • Can you walk us through a production ML system you've deployed end-to-end? What were the biggest challenges in moving from prototype to production?
  • Describe your experience building RAG pipelines or LLM-based applications. How did you optimize performance or handle common failure modes?
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