What are the responsibilities and job description for the Generative AI Engineer-Prompt Engineering position at Acadia Technologies, Inc.?
Job Details
1. Programming and Software Engineering:
- Python:Python is the dominant language for AI due to its readability, vast libraries (like TensorFlow, PyTorch), and ease of use.
- Programming Fundamentals:Understanding concepts like data structures, algorithms, object-oriented programming, and API interactions is necessary.
- IDE:Proficiency in using an Integrated Development Environment (IDE) is also important for developing and debugging AI applications.
2. Machine Learning and Deep Learning:
- Machine Learning Fundamentals:Understanding supervised learning, unsupervised learning, and reinforcement learning is essential for building generative models.
- Deep Learning:A strong understanding of deep learning techniques and architectures (like neural networks, convolutional neural networks, and recurrent neural networks) is required.
- Frameworks:Familiarity with deep learning frameworks like TensorFlow, PyTorch, and Keras is crucial.
- Model Evaluation:Knowledge of evaluation metrics (e.g., perplexity for language models) and techniques for assessing model performance is important.
3. Natural Language Processing (NLP):
- NLP Fundamentals:Understanding concepts like tokenization, stemming, and different NLP models (like RNNs, LSTMs) is essential for working with text data.
- NLP Libraries:Familiarity with NLP libraries and tools for text processing and analysis is also important.
4. Data Engineering:
- Data Processing and Cleaning:Skills in data preprocessing, handling missing values, and transforming data into a suitable format for AI models are essential.
- Data Structures and Algorithms:A good understanding of data structures (like dictionaries) and algorithms is needed for efficient data handling.
5. Mathematical and Statistical Skills:
- Mathematics:Understanding linear algebra, calculus, and probability is important for understanding the underlying mathematical principles of AI models.
- Statistics:A strong understanding of statistics is crucial for evaluating model performance and making data-driven decisions.
6. Model Deployment and Scaling:
- Deployment:Knowledge of deploying AI models into production environments (e.g., web applications, APIs) is necessary.
- Cloud Computing:Familiarity with cloud platforms like AWS, Google Cloud, and Azure is also beneficial.
7. Other Important Skills:
- Prompt Engineering:The ability to craft effective prompts to guide AI models is crucial for generative AI applications.
- Problem-Solving:Strong problem-solving skills are essential for designing and implementing AI solutions.
- Creativity:Generative AI often involves exploring creative solutions, so a creative mindset can be advantageous.
- Communication and Collaboration:Effective communication skills are needed for working with other engineers and stakeholders.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
Salary : $80,000
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