Job Posting Title

AI ML Data Science Engineer

Description
Location: Halifax, CA (Remote)

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

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