AI Engineer (Whisper,MLOps,Fine Tuning)
Job Description
FusionHit is seeking an experienced AI Engineer to lead a critical project focused on self-hosting and fine-tuning OpenAI's Whisper model for transcription and ambient listening applications. This role combines deep machine learning expertise with practical MLOps implementation, requiring a skilled practitioner who can deliver production-grade AI solutions. The position demands strong experience in speech-to-text systems, cloud infrastructure, and model deployment, with the goal of establishing a robust MLOps pipeline for model retraining and deployment. The ideal candidate should have 5+ years of experience in AI/ML engineering, possess strong Python skills, and be comfortable with tools like PyTorch or TensorFlow. This remote position requires residence in Latin America with significant overlap with Mountain Standard Time (MST). The role offers an opportunity to work on cutting-edge AI technology while delivering tangible business impact through a production-ready model by year-end. The position combines technical depth in machine learning with practical implementation skills, making it ideal for someone who wants to shape the future of speech recognition technology.
Responsibilities For AI Engineer (Whisper,MLOps,Fine Tuning)
- Fine-tune Whisper/WhisperX models for transcription and ambient listening tasks
- Deploy self-hosted Whisper models on Azure cloud infrastructure
- Design and implement an MLOps pipeline to support iterative training and deployment
- Ensure high data quality using existing audio + transcript datasets
- Collaborate on prompt engineering strategies for speech recognition improvements
- Deliver a production-ready model before year-end
Requirements For AI Engineer (Whisper,MLOps,Fine Tuning)
- 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)
