AI ML Data Science Engineer

Job Posting Title: AI ML Data Science Engineer

Location: Halifax, CA (Remote)

Must have AI Skills:

1. NLP for healthcare: Specialized natural language processing techniques tailored for medical data.
2. Prompt Engineering: Crafting effective prompts for AI models, especially important for large language models (LLMs).
3. Multimodal Prompting: Designing prompts that work across different AI tools and models.
4. Evaluation and Refinement: Assessing AI outputs and refining prompts for better results.
5. Model Fine-Tuning: Adjusting pre-trained models to improve performance on specific tasks.
6. Speech Recognition: Converting spoken language into text.
7. Text-to-Speech: Generating spoken language from text.
8. Audio Signal Processing: Analyzing and manipulating audio signals.
9. Speech to Text Expertise: Advanced skills in converting speech to text accurately.
10. Sentiment & Tone Analysis Expertise: Analyzing emotions and tone in text data.
11. LLM Expertise: Working with large language models like GPT-4.
12. Computer Vision (image processing & OCR): Analyzing and interpreting visual data, including optical character recognition.
13. Embeddings Models (TensorFlow/Phoenix): Using embeddings for various ML tasks.
14. Expertise in Knowledge retrieval systems & LLM Integration with retrieval.
15. Recommendation Algorithms: Building systems to suggest items to users.
16. Neural Collaborative Filtering: Using neural networks for recommendation systems.
17. Neural Network: Designing & implementing models.
18. Basic Knowledge in Azure Databricks infrastructure.
19. Knowledge in Healthcare Domain.

Data Science and Machine Learning Skills:

1. Data Annotation & Labeling: Essential for creating high-quality training datasets.
2. Model Training: Building and training machine learning models.
3. Fine Tuning: Adjusting pre-trained models to improve performance on specific tasks.
4. Supervised & Unsupervised Learning: Techniques for both labeled and unlabeled data.
5. Risk Prediction (time series models - LSTMs, ARIMA) & Survival Analysis Techniques: Predicting future events and analyzing time-to-event data.
6. Model Evaluation, Selection & Fine-tuning: Assessing and optimizing model performance.
7. Dimensionality Reduction: Reducing the number of features in a dataset.
8. Vector Search Optimization: Enhancing search algorithms using vector representations.
9. Feature Engineering: Creating new features from raw data to improve model performance.
10. Data Drift Monitoring & Identification: Detecting changes in data distributions over time.
11. Synthetic Data Generation: Creating artificial data for training models when real data is scarce.
12. Basic Knowledge in Azure Databricks infrastructure.
13. Knowledge in Healthcare Domain.