AI Innovations Developer

7 days ago

moradabad, India beBeeArtificialIntelligence Full time

Job Title: AI/ML EngineerWe are seeking a skilled and experienced AI/ML Engineer with a passion for developing innovative solutions using conventional machine learning and generative AI techniques. The ideal candidate will have extensive experience building and optimizing ML models, fine-tuning generative AI models, and working with large datasets.Key Responsibilities:Develop and train AI models for various applications, including player performance prediction, game strategy optimization, injury risk assessment, and fan engagement analytics.Fine-tune pre-trained generative AI models using sports-specific datasets to generate insights, predictions, or content tailored to Cricket and Football.Design and implement a robust evaluation framework for generative AI models, including metrics for accuracy, relevance, coherence, and domain-specific performance in sports applications.Process and analyze large, complex datasets to prepare data for training both ML and generative AI models.Implement and optimize ML algorithms and generative AI architectures, ensuring seamless integration for Cricket and Football use cases.Train, fine-tune, and evaluate models to achieve high accuracy, scalability, and robustness, using metrics relevant to sports applications.Requirements:5+ years of experience 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.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.

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