AI/ML Engineer

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.