AI Engineer (Whisper,MLOps,Fine Tuning) at FusionHit

, , Mexico -

Full Time

Start Date

Immediate

Expiry Date

30 Dec, 25

Salary

0.0

Posted On

01 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Engineering, MLOps, Fine Tuning, Speech-to-Text, Whisper, WhisperX, Azure, Python, ML Libraries, Audio Datasets, Prompt Engineering, Communication Skills

Industry

IT Services and IT Consulting

Description

FusionHit is seeking an experienced AI Engineer to join our dynamic team and take ownership of a high-impact project. This role involves self-hosting and fine-tuning OpenAI’s Whisper model (ideally WhisperX) for transcription and ambient listening use cases. You’ll also establish a robust MLOps pipeline for model retraining and deployment in a production environment. The ideal candidate is a hands-on ML practitioner with a deep understanding of speech-to-text systems and cloud infrastructure. This is a mission-critical role with high visibility, where you'll help deliver a scalable, production-grade AI solution by year-end. Location: Must reside and have work authorization in Latin America. This is a freelancing opportunity. Availability: Must be available to work with significant overlap with Mountain Standard Time (MST). The Ideal Candidate Has: 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). Key Responsibilities: 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.

Responsibilities

The AI Engineer will fine-tune Whisper/WhisperX models for transcription and ambient listening tasks and deploy self-hosted Whisper models on Azure cloud infrastructure. They will also design and implement an MLOps pipeline to support iterative training and deployment.