ML Operations Specialist

1 semana atrás

Sorocaba, São Paulo, Brasil beBeeMachine Tempo inteiro R$60.000 - R$120.000

About the Position

We are seeking a highly skilled Machine Learning Ops Engineer to join our team. In this role, you will be responsible for ensuring the smooth operation and scalability of our AI initiatives through various critical tasks:

  • Infrastructure Management: Define and propose an infrastructure management stack that drives business objectives.
  • Troubleshooting and Optimization: Help identify and mitigate AI infrastructure issues and implement features to improve model training speed on specific hardware.
  • Platform Evaluation and Implementation: Evaluate and implement new AI training and development platforms.
  • Automation and Orchestration: Automate model training and checkpointing using MLOps tools and maintain containerization tools (Docker, Singularity) for reproducibility.
  • Deployment and Lifecycle Management: Facilitate the transfer and replication of models from R&D to production environments, manage the model lifecycle, implement model tracking, and ensure infrastructure remains compatible with evolving training packages.

The Ideal Candidate

We are looking for a pragmatic infrastructure engineer who loves solving deep tech problems and enabling great ML work. You'll thrive in this role if you bring:

  • 5+ years of hands-on experience with ML Ops tools such as SLURM, MLflow, Kubeflow, SageMaker, or Vertex AI.
  • Experience managing Kubernetes clusters and distributed training workloads at scale.
  • Proficiency with containerization (Docker, Singularity) and reproducible ML environments.
  • Familiarity with popular deep learning frameworks (PyTorch, TensorFlow) and how they operate at infra level.
  • Solid understanding of model lifecycle best practices (training, validation, deployment, tracking).
  • Strong scripting and automation skills in Python, Bash, or similar.
  • Comfort working closely with ML researchers to translate needs into scalable, production-grade systems.
  • A proactive mindset: you're excited to take ownership of infra problems others avoid.

Bonus Points

Experience with multi-node, hardware-optimized training setups (e.g., GPU clusters, TPUs), contributions to internal tools or open-source projects in the ML Infra space, and prior experience helping bring ML systems through regulatory, safety, or quality review stages are all valuable assets.

Why This Role Matters

You'll have the opportunity to work on meaningful products that make a measurable difference, enjoying a remote-first work environment where you can thrive. Our talented team is dedicated to excellence and cares deeply about their craft and each other. We prioritize growth, stability, variety, and a culture of doing things right.

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