Job Description

About the role

Sportz Interactive is committed to harnessing the power of AI to drive innovation across our sports technology offerings. We're looking for a Head of AI/ML & Data Science to lead and scale our end-to-end AI practice. This pivotal role will be responsible for establishing the strategic and technical foundation of our AI/ML initiatives-- spanning classical statistics, predictive modelling, computer vision, and generative AI.

As the AI/ML leader in a growing organization, you will have the opportunity to architect and implement cutting-edge solutions across LLM workflows (including RAG and fine-tuning), and Image/Video/Audio generative AI pipelines. You'll play a hands-on role in shaping the data science roadmap, building a high-performing team, and embedding intelligence into our core products and platforms.

Key Responsibilities

Define and execute a multi-year AI roadmap aligned to business objectives. Architect scalable training/fine-tuning pipelines using pre-trained and custom models. Build and mentor a cross-functional team of ML engineers, data scientists & MLOps specialists. Establish production-grade MLOps: versioning, CI/CD, monitoring, experiment tracking. Partner with stakeholders to translate use cases (e.g., CV inspection, LLM agents) into deliverables. Ensure model quality, fairness, compliance, and reproducibility.

Background and Experience

8+ years of software engineering experience, with 5+ years in ML/AI. Hands-on Python proficiency, with deep experience in PyTorch and/or TensorFlow (or equivalent frameworks). Proven experience training deep learning models from scratch, including dataset design, architecture selection, hyperparameter tuning, and end-to-end validation Proven experience applying computer vision techniques for image and video processing--object detection, segmentation, inpainting, style transfer, temporal coherence optimization. Strong background across the ML lifecycle: data preparation, feature engineering, modeling, deployment, and monitoring. Experience with RAG and LLM fine-tuning workflows. Proven track record building end-to-end MLOps platforms (e.g., Kubeflow, MLflow, TFX, SageMaker, or similar), including model versioning, CI/CD for training and serving, experiment tracking, and monitoring * Excellent leadership, communication, and stakeholder management skills.