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

, , Colombia -

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, PyTorch, TensorFlow, Audio Datasets, Preprocessing Techniques, 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.