AI/ML Specialist for Sports Technology

1 day ago

kozhikode, India beBeeSports Full time

Are you passionate about leveraging cutting-edge AI and ML techniques to drive innovative solutions in sports?About the RoleWe're seeking a skilled AI/ML Engineer to join our team and contribute to the development of Sports Companion GPT - Aiko, a groundbreaking platform for Cricket and Football applications.The ideal candidate will have over 5 years of experience in conventional machine learning and expertise in generative AI. You'll be responsible for designing and developing AI models that combine traditional ML techniques with generative AI models for Cricket and Football applications.Key ResponsibilitiesModel Development: Design and develop AI models for Sports Companion GPT - Aiko, combining conventional ML techniques with generative AI models for Cricket and Football applications.Generative AI Fine-Tuning: Fine-tune pre-trained generative AI models using Cricket and Football-specific datasets to generate insights, predictions, or content tailored to these sports.Gen AI Evaluation Framework: Develop and implement a robust evaluation framework for generative AI models, including metrics for accuracy, relevance, coherence, and domain-specific performance in sports applications.Data Processing: Process and analyze large, complex datasets to prepare data for training both ML and generative AI models.Algorithm Development: Implement and optimize ML algorithms and generative AI architectures, ensuring seamless integration for Cricket and Football use cases.Model Training & Evaluation: Train, fine-tune, and evaluate models to achieve high accuracy, scalability, and robustness, using metrics relevant to sports applications.Collaboration: Work with sports analysts, data engineers, and domain experts across India, the UK, Singapore, and the Middle East to incorporate domain knowledge into Aiko and align with business objectives.Deployment & Optimization: Deploy models into production environments, ensuring efficient inference for real-time or near-real-time sports applications.Research: Stay updated on advancements in ML and generative AI, applying relevant techniques to improve model performance and evaluation frameworks.Documentation: Document model architectures, fine-tuning processes, evaluation frameworks, and performance metrics for reproducibility and knowledge sharing.Ethical AI: Ensure models are developed with fairness, transparency, and ethical considerations, mitigating biases in predictions or generated outputs.

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