Entry-Level Artificial Intelligence Specialist

2 months ago

Atlanta, United States RSM Atlanta LLC Full time
Job DescriptionJob Description

This is a remote position.

1. Data Collection and Preprocessing:
  • Gather and pre-process data sets for training machine learning models.
  • Clean, transform, and format data to ensure its suitability for analysis and modeling.
2. Model Development and Training:
  • Assist in the development and implementation of machine learning algorithms and models.
  • Train and fine-tune models using appropriate techniques and methodologies.
3. Evaluation and Testing:
  • Evaluate model performance using relevant metrics and validation techniques.
  • Conduct testing to assess the robustness and generalization ability of AI models.
4. Research and Analysis:
  • 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.
5. Collaboration and Communication:
  • Collaborate with team members and stakeholders to understand project requirements and objectives.
  • Communicate findings, insights, and recommendations effectively to technical and non-technical audiences.
6. Documentation and Reporting:
  • Document processes, methodologies, and results in a clear and organized manner.
  • Prepare reports, presentations, and documentation to share findings and progress with relevant stakeholders.
7. Continuous Learning and Development:
  • 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.
8. Problem-Solving and Troubleshooting:
  • 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.
9. Ethical and Legal Considerations:
  • 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.
10. Support and Maintenance:
  • 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.


Requirements1. 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.

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