AI Model Developer for Sports Technology

As a pioneering AI/ML Engineer, you will play a pivotal role in shaping the future of sports technology by designing and developing cutting-edge AI models for Sports Companion GPT - Aiko. With a strong background in conventional machine learning and experience or interest in generative AI, you will work closely with our global teams to create an AI model that delivers actionable insights and innovative solutions for Cricket and Football applications.Key Responsibilities:• Model Development: Develop sophisticated AI models combining conventional ML techniques (e.g., regression, classification, clustering) with generative AI models for player performance prediction, game strategy optimization, injury risk assessment, or fan engagement analytics.• 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, if applicable.• 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 (e.g., player statistics, video analysis, sensor data, or text data) 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, preferably leveraging Azure cloud platforms, 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.Qualifications: Experience:• 5+ years in AI/ML engineering, with significant experience in conventional ML.• Proven expertise in building ML models (e.g., Scikit-learn, XGBoost).• Experience with fine-tuning generative AI models (e.g., Hugging Face, TensorFlow, PyTorch) is a plus but not mandatory.• Previous experience with sports-related datasets, particularly Cricket or Football, is a plus but not mandatory.• Technical Skills:• Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).• Strong knowledge of conventional ML algorithms (e.g., regression, decision trees, SVMs) and familiarity with generative AI architectures (e.g., transformers, GANs).• Experience with data preprocessing, feature engineering, and handling large datasets.• Familiarity with cloud platforms for model training and deployment, with Azure experience as a strong plus.• Knowledge of MLOps practices, including model versioning, monitoring, and CI/CD pipelines.• Experience with computer vision, natural language processing, or text generation is a plus.• Domain Knowledge: Familiarity with Cricket or Football analytics, biomechanics, or performance metrics is an advantage but not required.• Soft Skills:• Strong problem-solving and analytical skills.• Effective communication and collaboration skills for cross-functional and global teamwork.• Ability to explain technical concepts to non-technical stakeholders.Preferred Qualifications:• Experience fine-tuning large language models or generative AI for domain-specific applications.• Experience developing evaluation frameworks for generative AI models.• Familiarity with Azure cloud services (e.g., Azure Machine Learning, Azure Kubernetes Service, or Azure Data Lake) for model development and deployment.• Experience with real-time or streaming data for sports applications.• Knowledge of reinforcement learning or multimodal AI models.• Familiarity with sports technologies, such as player tracking systems or wearables.• Contributions to open-source AI/ML projects or published research in relevant fields.