- Gather and pre-process data sets for training machine learning models.
- Clean, transform, and format data to ensure its suitability for analysis and modeling.
- Assist in the development and implementation of machine learning algorithms and models.
- Train and fine-tune models using appropriate techniques and methodologies.
- Evaluate model performance using relevant metrics and validation techniques.
- Conduct testing to assess the robustness and generalization ability of AI models.
- Stay updated on the latest developments and advancements in artificial intelligence and machine learning.
- Conduct research and analysis to identify opportunities for improving AI applications and algorithms.
- Collaborate with team members and stakeholders to understand project requirements and objectives.
- Communicate findings, insights, and recommendations effectively to technical and non-technical audiences.
- Document processes, methodologies, and results in a clear and organized manner.
- Prepare reports, presentations, and documentation to share findings and progress with relevant stakeholders.
- Continuously enhance knowledge and skills in artificial intelligence, machine learning, and related technologies.
- Participate in training programs, workshops, and professional development opportunities to stay current in the field.
- Identify and address issues or challenges encountered during the development and implementation of AI solutions.
- Troubleshoot errors, bugs, and performance issues to ensure the reliability and effectiveness of AI systems.
- Adhere to ethical guidelines and principles in the development and deployment of AI technologies.
- Ensure compliance with relevant regulations and laws governing data privacy, security, and ethical use of AI.
- Provide support and maintenance for AI systems, including troubleshooting issues and implementing updates or enhancements as needed.
- Monitor system performance and stability, and implement measures to optimize efficiency and reliability.
- High School Diploma or equivalent.
- Coursework or specialization in artificial intelligence, machine learning, data science, or computer vision.
- Proficiency in at least one programming language commonly used in AI and machine learning such as Python, R, or Java.
- Experience with libraries and frameworks like TensorFlow, PyTorch, sci-kit-learn, or Keras.
- Strong foundation in statistics, linear algebra, calculus, and probability theory.
- Ability to apply mathematical concepts to develop and interpret machine learning algorithms.
- Familiarity with data collection, cleaning, preprocessing, and transformation techniques.
- Experience working with structured and unstructured data sets for AI model development.
- Understanding of various machine learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning.
- Knowledge of common algorithms like linear regression, logistic regression, decision trees, random forests, SVM, k-means clustering, etc.
- Basic understanding of deep learning concepts and architectures, including artificial neural networks, Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning.
- Experience with deep learning frameworks like TensorFlow or PyTorch.
- Ability to evaluate and validate machine learning models using appropriate metrics and validation techniques.
- Familiarity with techniques such as cross-validation, hyper-parameter tuning, and model selection.
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Entry-Level Artificial Intelligence Specialist - india, India - RSM Atlanta LLC
1 month ago
Description
Job DescriptionThis is a remote position.
1. Data Collection and Preprocessing:9. Excellent communication skills and strong attention to detail.
Requirements
1. Educational Background: High School Diploma or equivalent. Coursework or specialization in artificial intelligence, machine learning, data science, or computer vision. 2. Programming Skills: Proficiency in at least one programming language commonly used in AI and machine learning such as Python, R, or Java. Experience with libraries and frameworks like TensorFlow, PyTorch, sci-kit-learn, or Keras. 3. Statistical and Mathematical Understanding: Strong foundation in statistics, linear algebra, calculus, and probability theory. Ability to apply mathematical concepts to develop and interpret machine learning algorithms. 4. Data Handling and Preprocessing: Familiarity with data collection, cleaning, preprocessing, and transformation techniques. Experience working with structured and unstructured data sets for AI model development. 5. Machine Learning Algorithms: Understanding of various machine learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning. Knowledge of common algorithms like linear regression, logistic regression, decision trees, random forests, SVM, k-means clustering, etc. 6. Deep Learning: Basic understanding of deep learning concepts and architectures, including artificial neural networks, Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning. Experience with deep learning frameworks like TensorFlow or PyTorch. 7. Model Evaluation and Validation: Ability to evaluate and validate machine learning models using appropriate metrics and validation techniques. Familiarity with techniques such as cross-validation, hyper-parameter tuning, and model selection. 8. Strong Analytical and problem-solving skills to identify and address challenges in AI projects. 9. Excellent communication skills and strong attention to detail.
