Skyline Products, Inc. ยท 4 hours ago
AI Predictive Machine Learning Engineer
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Responsibilities
Design, develop, and implement AI-driven solutions using machine learning techniques, deep learning, natural language processing (NLP), and computer vision
Build, train, and optimize machine learning models to analyze large datasets and derive actionable insights
Develop and integrate machine learning algorithms into production systems, ensuring scalability and performance
Collaborate with data scientists, software engineers, and other stakeholders to define system requirements, data pipelines, and performance metrics
Utilize big data tools and frameworks to process, analyze, and extract insights from large datasets in various domains
Continuously improve model performance through experimentation, hyperparameter tuning, and model evaluation
Stay updated with the latest AI and machine learning research trends to apply cutting-edge technologies and techniques
Implement best practices in coding, version control, and continuous integration for AI/ML projects
Troubleshoot and resolve issues related to machine learning models and AI applications
Qualification
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Required
Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Keras, Scikit-learn)
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field
Strong proficiency in programming languages such as Python, Java, or C++
Solid understanding of AI techniques, including supervised and unsupervised learning, reinforcement learning, NLP, and deep learning
Proficiency in data manipulation and analysis using tools such as Pandas, NumPy, and SQL
Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) for deploying AI/ML models
Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes)
Strong problem-solving and analytical skills, with the ability to work on complex data-driven projects
Excellent communication skills and the ability to work collaboratively in a multidisciplinary team
Preferred
Familiarity with data engineering concepts and tools, including ETL processes and data warehousing
Knowledge of distributed computing and high-performance computing (HPC) for scaling machine learning models
Familiarity with Agile methodologies and version control systems (e.g., Git)