Skyline Products, Inc. ยท 5 hours ago
AI Predictive Machine Learning Engineer
Maximize your interview chances
ElectronicsManufacturing
Growth Opportunities
Insider Connection @Skyline Products, Inc.
Get 3x more responses when you reach out via email instead of LinkedIn.
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
Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.
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).