FusionHit

AI Engineer, Whisper, MLOps, Fine Tuning

FusionHit

contract

Posted on:

Origin:  • 🇨🇷 Costa Rica

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Job Level

Mid-LevelSenior

Tech Stack

AzureCloudPythonPyTorchTensorflow

About the role

  • Self-host and fine-tune OpenAI’s Whisper (ideally WhisperX) for transcription and ambient listening use cases.
  • Establish and implement a robust MLOps pipeline for iterative model retraining and production deployment.
  • Deploy self-hosted Whisper models on Azure cloud infrastructure (self-hosted, not managed services).
  • Ensure high data quality using existing audio and transcript datasets.
  • Collaborate on prompt engineering strategies to improve speech recognition.
  • Deliver a scalable, production-grade AI solution by year-end and take ownership of the project.

Requirements

  • BS/MS in Computer Science, Machine Learning, or related field with 5+ years of experience in AI/ML engineering.
  • Deep experience with speech-to-text models such as Whisper or WhisperX.
  • Proven expertise in fine-tuning ML models with labeled datasets.
  • Strong experience in MLOps using tools like MLflow, Kubeflow, or similar frameworks.
  • Hands-on experience deploying models on Azure (self-hosted, not managed services).
  • Proficiency in Python and ML libraries like PyTorch or TensorFlow.
  • Experience working with audio datasets and preprocessing techniques.
  • Familiarity with prompt engineering related to speech-based AI solutions.
  • Excellent communication skills in English (C1 preferred, strong B2 may be considered).
  • Must reside and have work authorization in Latin America.
  • Must be available to work with significant overlap with Mountain Standard Time (MST).
  • This is a freelancing opportunity.